Search Results for "augmented" - Page 2
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Showing 149 open source projects for "augmented"
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Aix-DB
Based on the LangChain/LangGraph framework
...The system is designed as a ChatBI solution that allows users to query datasets using natural language and receive structured insights, charts, and visualizations automatically. Built on frameworks such as LangChain and LangGraph, Aix-DB integrates retrieval-augmented generation and Text-to-SQL capabilities to convert user questions into executable database queries. The platform supports multiple types of data sources and provides an end-to-end pipeline that includes intent recognition, SQL generation, database execution, and visual presentation of results. Its architecture includes multiple layers such as a web interface, API gateway, AI service layer, and data storage layer that support relational databases, vector stores, graph databases, and file systems. -
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Kernel Memory
Research project. A Memory solution for users, teams, and applications
...The project focuses on enabling applications to store, index, and retrieve information so that AI systems can incorporate external knowledge when generating responses. It supports scenarios such as document ingestion, semantic search, and retrieval-augmented generation, allowing language models to answer questions using contextual information from private or enterprise datasets. Kernel Memory can ingest documents in multiple formats, process them into embeddings, and store them in searchable indexes. Applications can then query these indexed data sources to retrieve relevant information and include it as context for AI responses. -
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Vanna 2.0
Chat with your SQL database
Vanna is an open-source Python framework that enables natural language interaction with databases by converting user questions into executable SQL queries using large language models. The framework uses a retrieval-augmented generation architecture that learns from database schemas, documentation, and past query examples to generate accurate queries tailored to a specific dataset. Vanna can be integrated into many environments, including notebooks, web applications, messaging platforms, and data dashboards, making it flexible for analytics and data exploration workflows. ... -
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LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
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DeepAudit
AI multi-agent platform for automated code security auditing system
...It also includes automated proof-of-concept validation using a sandboxed environment, allowing detected issues to be tested for real exploitability. DeepAudit integrates retrieval-augmented generation techniques to enhance contextual understanding and reduce false positives during analysis. Users can import projects and trigger a full audit workflow that includes risk identification, exploit generation, validation, and final report creation. -
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Qwen-Agent
Agent framework and applications built upon Qwen>=3.0
Qwen-Agent is a framework for building applications / agents using Qwen models (version 3.0+). It provides components for instruction following, tool usage (function calling), planning, memory, RAG (retrieval augmented generation), code interpreter, etc. It ships with example applications (Browser Assistant, Code Interpreter, Custom Assistant), supports GUI front-ends, backends, server setups. Agent workflow can maintain context / memory to perform multi-turn or more complex logic over time. It acts as the backend for Qwen Chat among other use cases. ... -
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WeKnora
LLM framework for document understanding and semantic retrieval
...It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. This approach enables the system to provide more reliable answers by grounding model reasoning in the content of uploaded documents. WeKnora is designed with a modular architecture that separates components for document processing, search strategies, and model inference, allowing developers to customize or extend different parts of the pipeline. ... -
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Dynamiq
An orchestration framework for agentic AI and LLM applications
...The framework supports the creation of multi-agent systems where different AI agents collaborate to solve tasks such as information retrieval, document analysis, or automated decision making. Dynamiq also includes built-in support for retrieval-augmented generation pipelines that allow models to access external documents and knowledge bases during inference. -
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Generative AI for beginners with JS
Join a time-traveling adventure where you meet history’s legends
...Each lesson includes written explanations, hands-on exercises, quizzes, and supporting videos to help developers learn the material progressively. Topics covered include prompt engineering, building AI-powered applications, working with structured outputs, integrating retrieval-augmented generation, and enabling tool or function calling in AI systems. The repository focuses specifically on how generative AI can be integrated into web, mobile, or desktop applications using JavaScript frameworks and APIs. -
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RAG from Scratch
Demystify RAG by building it from scratch
RAG From Scratch is an educational open-source project designed to teach developers how retrieval-augmented generation systems work by building them step by step. Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. ... -
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LangChain Rust
LangChain for Rust, the easiest way to write LLM-based programs
...By adapting LangChain concepts to the Rust programming language, the project emphasizes performance, safety, and efficient memory management. Developers can use the framework to build chatbots, autonomous agents, and knowledge-augmented AI systems that interact with external data sources. The library provides abstractions for model providers, prompt templates, conversation memory, and vector search integrations. It also enables the construction of multi-step pipelines where LLM outputs feed into subsequent actions or tool calls. -
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Reader LLM
Convert any URL to an LLM-friendly input with a simple prefix
...Developers can use a simple URL prefix to retrieve a version of a webpage that has been optimized for machine consumption, making it suitable for use in AI agents or retrieval-augmented generation pipelines. In addition to converting individual pages, the service can perform web searches and return relevant content that can be ingested directly by AI systems. The tool relies on specialized models and parsing techniques to handle complex HTML structures and extract meaningful content while preserving important context. -
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AIPex
AI browser automation assistant, no migration and privacy first
AIPex is an AI-augmented development toolkit and workflow platform that aims to accelerate software productivity by integrating intelligent assistants, code generation tools, and customizable automation patterns directly into developer workflows. Rather than treating AI as a separate helper, AIPex embeds AI capabilities into common tasks like scaffolding components, generating tests, analyzing code quality, and performing refactors, allowing developers to stay in flow while benefiting from model-assisted insights. ... -
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GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
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Hands-On Large Language Models
Official code repo for the O'Reilly Book
...The repository is structured into chapters that align with the educational progression of the book — covering everything from foundational topics like tokens, embeddings, and transformer architecture to advanced techniques such as prompt engineering, semantic search, retrieval-augmented generation (RAG), multimodal LLMs, and fine-tuning. Each chapter contains executable Jupyter notebooks that are designed to be run in environments like Google Colab, making it easy for learners to experiment interactively with models, visualize attention patterns, implement classification and generation tasks. -
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AWS GenAI LLM Chatbot
A modular and comprehensive solution to deploy a Multi-LLM
AWS GenAI LLM Chatbot is an enterprise-ready reference solution for deploying a secure, feature-rich generative AI chatbot on AWS with retrieval-augmented generation capabilities. The project is built as a modular blueprint that helps organizations stand up a production-oriented chat experience rather than a simple demo, combining model access, knowledge retrieval, storage, security, and user interface components into one deployable system. It supports multiple model providers and endpoints, giving teams flexibility to work with Amazon Bedrock, SageMaker-hosted models, and additional model access patterns through related integrations. ... -
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second-brain-ai-assistant-course
Learn to build your Second Brain AI assistant with LLMs
...The concept of a “second brain” refers to a personal knowledge repository containing notes, research, and documents that can be queried and analyzed using AI. Through a series of modules, the project explains how to design data pipelines, build retrieval-augmented generation systems, and implement agent-based reasoning workflows. The course also introduces practical techniques such as dataset generation, model fine-tuning, and deployment strategies for AI applications. Learners build a full system capable of retrieving information from stored resources and generating responses based on that data. -
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Agentic RAG for Dummies
A modular Agentic RAG built with LangGraph
Agentic RAG for Dummies is an educational repository that demonstrates how to build retrieval-augmented generation systems combined with autonomous AI agents. The project explains the principles behind agentic retrieval pipelines where language models can dynamically decide when to retrieve information, analyze results, and plan further actions. Instead of relying on static retrieval pipelines, the system shows how agents can orchestrate retrieval, reasoning, and tool usage in a more flexible decision loop. ... -
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Streamer-Sales
LLM Large Model of Selling Anchor
...By analyzing product characteristics and marketing information, the model can produce engaging explanations that emphasize benefits, features, and emotional appeal to encourage viewers to make purchasing decisions. The system integrates multiple AI technologies including retrieval-augmented generation to incorporate product knowledge, speech synthesis to convert generated scripts into voice output, and digital human generation to create virtual hosts. It also supports automatic speech recognition and agent-based tools that can retrieve additional information such as logistics or product details during live sessions. -
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NVIDIA Generative AI Examples
Generative AI reference workflows
...The project is designed to help developers accelerate the development of AI applications by providing ready-to-run pipelines, notebooks, and tools that demonstrate how to integrate large language models into real-world systems. The repository includes examples covering topics such as retrieval-augmented generation pipelines, agent-based workflows, and multimodal AI applications that combine text, vision, and data processing. Many of the examples show how to deploy AI services using containerized environments, GPU acceleration, and microservices that can scale across modern infrastructure. Developers can explore sample chatbot applications, document question-answering systems, and knowledge-base pipelines that illustrate how generative AI can interact with external data sources. -
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Chipper
AI interface for tinkerers (Ollama, Haystack RAG, Python)
Chipper is an AI interface designed for tinkerers and developers, providing a platform to experiment with various AI models and techniques. It offers integration with tools like Ollama and Haystack for Retrieval-Augmented Generation (RAG), enabling users to build and test AI applications efficiently. Chipper supports Python and provides a modular architecture, allowing for customization and extension based on specific project requirements. -
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Open WebUI
User-friendly AI Interface
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with a built-in inference engine for Retrieval Augmented Generation (RAG), making it a powerful AI deployment solution. Key features include effortless setup via Docker or Kubernetes, seamless integration with OpenAI-compatible APIs, granular permissions and user groups for enhanced security, responsive design across devices, and full Markdown and LaTeX support for enriched interactions. ... -
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Step-Audio 2
Multi-modal large language model designed for audio understanding
...It integrates a latent-space audio encoder, discrete acoustic tokens, and reinforcement-learning–based training (CoT + RL) to enhance its ability to capture and reproduce voice styles, intonations, and subtle vocal cues. Moreover, Step-Audio2 supports tool-calling and retrieval-augmented generation (RAG), allowing it to access external knowledge sources or audio/text databases, thus reducing hallucinations and improving coherence in complex dialogues. -
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Kimi Code CLI
Kimi Code CLI is your next CLI agent
...It supports an interactive shell-like user interface where you can chat with the agent, request code edits, run shell commands, and receive contextual suggestions as you work, creating a seamless blend of AI-augmented development and traditional terminal usage. The tool includes integration with Zsh so that users can activate AI assistance via a hotkey while staying within their favorite shell environment, and it can serve as an Agent Client Protocol (ACP) server to bridge AI functionality into compatible IDEs and editors. Its support for well-established MCP tool configuration conventions lets developers connect the CLI to external tools and services during workflows, expanding its capabilities beyond simple queries into orchestrated development tasks. -
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RAG API
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL
rag_api is an open-source REST API for building Retrieval-Augmented Generation (RAG) systems using LLMs like GPT. It lets users index documents, search semantically, and retrieve relevant content for use in generative AI workflows. Designed for rapid prototyping, it is ideal for chatbot development, document assistants, and knowledge-based LLM apps.
