Search Results for "augmented" - Page 4
Sort By:
Showing 228 open source projects for "augmented"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
MindWork AI Studio
Independent cross-platform desktop app for local and cloud LLMs
...The platform introduces a concept of “assistants,” which abstract prompting into reusable tools for tasks like translation, summarization, or document analysis, making it easier for non-technical users to leverage AI capabilities. It also incorporates advanced features such as retrieval-augmented generation, plugin extensibility, and support for multiple data sources, allowing users to integrate their own files and knowledge bases into conversations. -
2
AgentGuide
AI Agent Development Guide, LangGraph in Action, Advanced RAG
...Instead of presenting scattered resources, the repository organizes them into a systematic learning roadmap that guides learners from foundational concepts to advanced AI agent systems. The guide covers topics such as agent frameworks, retrieval-augmented generation systems, multi-agent collaboration, memory management, and tool usage. It also includes practical projects, interview preparation materials, and curated research papers related to AI agents and LLM engineering. The project is designed not only for learning but also for career preparation, helping developers understand how to build portfolio projects and prepare for AI engineering roles. -
3
VideoRAG
"VideoRAG: Chat with Your Videos
VideoRAG is a retrieval-augmented generation (RAG) framework tailored for video content that enables AI systems to answer questions, summarize, and reason over long videos by combining visual embeddings with contextual search. The system works by first breaking video into clips, extracting visual and audio-textual features, and indexing them into embeddings, then using an LLM with a retriever to pull relevant segments on demand. -
4
TruLens
Evaluation and Tracking for LLM Experiments
...An easy-to-use interface that allows developers to compare different versions of their applications, facilitating informed decision-making and optimization. TruLens supports various use cases, including question-answering, summarization, retrieval-augmented generation, and agent-based applications. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
5
Generative AI Examples
Generative AI Examples is a collection of GenAI examples
...Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines. Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows. A four-step assessment for grading generative AI systems around performance, features, trustworthiness and enterprise-grade readiness. -
6
zola (né Gutenberg)
Fast static site generator in a single binary with everything built-in
A fast static site generator in a single binary with everything built in. Forget dependencies. Everything you need in one binary. Zola comes as a single executable with Sass compilation, syntax highlighting, table of contents and many other features that traditionally require setting up a dev environment or adding some JavaScript libraries to your site. The average site will be generated in less than a second, including Sass compilation and syntax highlighting. Zola renders your whole site... -
7
Spring AI Alibaba Examples
Spring AI Alibaba examples for building and testing AI apps
...It is designed to help developers understand core concepts, explore practical implementations, and follow best practices when building AI-powered systems using the Spring ecosystem. Each module focuses on a specific use case such as chat, image processing, audio handling, graph workflows, and retrieval-augmented generation. The examples highlight how to integrate AI models, manage prompts, handle memory, and build multi-model or multi-agent workflows. Developers can explore individual project folders for detailed instructions and implementation guidance. Spring AI Alibaba Examples also supports experimentation through playground modules and encourages contributions to expand real-world AI use cases and improve development practices. -
8
kg-gen
Knowledge Graph Generation from Any Text
...The framework addresses common problems in automatic knowledge graph construction, particularly sparsity and duplication of entities, by applying a clustering and entity-resolution process that merges semantically similar nodes. This allows the generated graphs to be denser, more coherent, and easier to use for downstream tasks such as retrieval-augmented generation, semantic search, and reasoning systems. -
9
Flock
Flock is a workflow-based low-code platform for building chatbots
Flock is a workflow-based low-code platform designed for building AI applications such as chatbots, retrieval-augmented generation systems, and multi-agent workflows. The platform uses a visual workflow architecture where different nodes represent processing steps such as input processing, model inference, retrieval operations, and tool execution. Developers can connect these nodes to create complex pipelines that orchestrate multiple language models and external services. -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
-
10
Glean
A self-hosted RSS reader and personal knowledge management tool
...With a modern responsive UI and optional admin dashboard, Glean balances readability with lightweight performance, making it suited for both individual readers and teams. Its knowledge management capabilities are augmented by features like smart filtering, “read later” queues, and future plans for AI-driven summaries and preference-based recommendations. -
11
Easy DataSet
A powerful tool for creating datasets for LLM fine-tuning
Easy DataSet is a comprehensive open-source tool designed to make creating high-quality datasets for large language model fine-tuning, retrieval-augmented generation (RAG), and evaluation as easy and automated as possible by providing intuitive interfaces and powerful parsing, segmentation, and labeling tools. It supports ingesting domain-specific documents in a wide range of formats — including PDF, Markdown, DOCX, EPUB, and plain text — and can intelligently segment, clean, and structure content into rich datasets tailored for downstream LLM training needs. ... -
12
LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when... -
13
Watchdog
Python library and shell utilities to monitor filesystem events
...You can use the shell-command subcommand to execute shell commands in response to events. watchmedo can read tricks.yaml files and execute tricks within them in response to file system events. Tricks are actually event handlers that subclass watchdog.tricks.Trick and are written by plugin authors. Trick classes are augmented with a few additional features that regular event handlers don't need. The directory containing the tricks.yaml file will be monitored. Each trick class is initialized with its corresponding keys in the tricks.yaml file as arguments and events are fed to an instance of this class as they arrive. -
14
LangChain for .NET
C# implementation of LangChain
...It also provides both low-level async APIs and higher-level chain abstractions, giving developers flexibility in how they structure their applications. With built-in support for retrieval-augmented generation and structured pipelines, LangChain .NET is designed for production-ready AI systems. -
15
LangChainGo
LangChain for Go, the easiest way to write LLM-based programs in Go
...It supports multiple providers including OpenAI, Anthropic, Google, and local models, offering a unified interface for interacting with different backends. LangChainGo also includes support for embeddings, semantic search, and retrieval-augmented generation, enabling developers to build data-aware applications. With built-in abstractions for agents and tool usage, it enables the creation of systems that can reason, take actions, and interact with their environment. -
16
Claude Cookbooks
A collection of notebooks/recipes showcasing ways of using Claude
...It serves as both a learning resource and a reference library, helping developers understand how to apply AI capabilities such as classification, summarization, and retrieval-augmented generation in real-world scenarios. The repository includes structured examples for integrating Claude with external tools, databases, and APIs, showcasing how to extend its functionality beyond basic text generation. It also covers advanced techniques like sub-agent orchestration, prompt optimization, and automated evaluation workflows. ... -
17
Code-Graph-RAG
The ultimate RAG for your monorepo
Code-Graph-RAG is an advanced retrieval-augmented generation system designed specifically for understanding and interacting with large, multi-language codebases by transforming them into structured knowledge graphs. It uses Tree-sitter to parse source code into abstract syntax trees, extracting relationships between functions, classes, and modules to build a graph-based representation of the entire codebase. -
18
model2Vec
Fast State-of-the-Art Static Embeddings
...By using a distillation-based approach, it can produce lightweight models that run efficiently on CPUs, making it suitable for edge applications and large-scale processing pipelines. The resulting models can be used for a wide range of tasks, including semantic search, clustering, classification, and retrieval-augmented generation systems. One of its key advantages is its simplicity, as it requires minimal dependencies and can generate embeddings extremely quickly compared to traditional transformer-based approaches. -
19
LitServe
Minimal Python framework for scalable AI inference servers fast
LitServe is a minimal Python framework designed for building custom AI inference servers with full control over how models are executed and served. It allows developers to define their own inference logic, making it suitable for complex systems such as multi-model pipelines, agents, and retrieval-augmented generation workflows. Unlike traditional serving tools that enforce rigid abstractions, LitServe focuses on flexibility by letting users control request handling, batching strategies, and output processing directly in Python. LitServe is built on top of FastAPI and extends it with AI-specific optimizations such as efficient multi-worker execution, which can significantly improve throughput. ... -
20
Cognita
Open source RAG framework for building scalable modular AI apps
Cognita is an open source framework designed to help developers build, organize, and deploy Retrieval-Augmented Generation (RAG) applications in a structured and production-ready way. It addresses the gap between quick experimentation in notebooks and the complexity of deploying scalable AI systems by introducing a modular and API-driven architecture. Cognita provides reusable components such as parsers, data loaders, embedders, retrievers, and query controllers, allowing teams to customize each stage of the RAG pipeline independently. ... -
21
Text Embeddings Inference
High-performance inference server for text embeddings models API layer
...It focuses on delivering fast and scalable embedding generation by leveraging optimized inference techniques and modern hardware acceleration. It is built to support transformer-based embedding models, making it suitable for tasks such as semantic search, clustering, and retrieval-augmented systems. It provides an API interface that allows developers to integrate embedding capabilities into applications without managing model internals directly. Text Embeddings Inference is optimized for throughput and low latency, enabling it to handle large volumes of requests reliably. It also emphasizes ease of deployment, often using containerization and configurable runtime options to adapt to different infrastructure setups. -
22
AI Engineering Academy
Mastering Applied AI, One Concept at a Time
...The project aims to make complex AI concepts accessible by structuring them into progressive learning modules covering topics such as prompt engineering, retrieval-augmented generation, LLM deployment, and AI agents. Rather than focusing purely on theoretical explanations, the repository emphasizes hands-on understanding of how modern AI systems are designed, built, and deployed in real-world applications. It aggregates tutorials, conceptual explanations, diagrams, and example workflows that guide learners through the process of creating AI-powered products. ... -
23
KG-LLM-Papers
Papers integrating knowledge graphs (KGs) and large language models
...The repository functions as a continuously updated index of scholarly work that investigates how structured knowledge representations can enhance the reasoning, factual accuracy, and interpretability of language models. It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge graph-guided prompting, retrieval-augmented generation, reasoning over structured data, and hybrid architectures combining symbolic and neural systems. By gathering these papers into a single organized repository, the project helps researchers quickly discover relevant literature and track the evolution of the field. -
24
rag-search
RAG Search API
rag-search is a lightweight Retrieval-Augmented Generation API service designed to provide structured semantic search and answer generation through a simple FastAPI backend. The project integrates web search, vector embeddings, and reranking logic to retrieve relevant context before passing it to a language model for response generation. It is built to be easily deployable, requiring only environment configuration and dependency installation to run a functional RAG service. -
25
Pathway AI Pipelines
Ready-to-run cloud templates for RAG
Pathway AI Pipelines is a collection of ready-to-deploy AI pipeline templates designed to help developers rapidly build production-grade retrieval-augmented generation and enterprise search applications. The project provides end-to-end examples that connect live data sources to LLM workflows, enabling applications to stay synchronized with continuously changing information. It supports numerous connectors including local files, Google Drive, SharePoint, Kafka, PostgreSQL, and real-time APIs, making it suitable for enterprise data environments. ...
