Latest Open Source Projects
RAG_Techniques
RAG_Techniques
TLDR: This repository is a comprehensive collection of advanced Retrieval-Augmented Generation (RAG) techniques. It includes various methods for enhancing RAG systems such as query enhancement, context enrichment, advanced retrieval methods, iterative and adaptive techniques, evaluation, explainability, and advanced architectures.
cline
cline
TLDR: Cline is an AI assistant for the CLI and editor. It can handle complex software development tasks, use various APIs and models, run commands in the terminal, create and edit files, use the browser, and add custom tools through the Model Context Protocol.
aisuite
aisuite
TLDR: A Python package called aisuite provides a simple, unified interface to multiple Generative AI providers. It allows developers to use multiple LLM through a standardized interface similar to OpenAI's, enabling easy interaction and comparison of results. Currently supports various providers and offers installation options. Comes with an MIT license and welcomes contributions.
RAG
RAG
TLDR: An implementation of the paper 'Searching for Best Practices in Retrieval-Augmented Generation'. Provides installation instructions and a way to run evaluations. If used, requires citing the associated paper.
Building-llama3-from-scratch
Building-llama3-from-scratch
TLDR: This repository contains code to build the LLaMA 3 language model from scratch using Python. It explains the components of LLaMA 3 such as pre-normalization using RMSNorm, SwiGLU activation function, Rotary Embeddings (RoPE), and Byte Pair Encoding (BPE) Algorithm. The code shows how to tokenize input data, create embeddings for each token, implement attention heads, self-attention, multi-head attention, SwiGLU activation function, and generate the output.
multiagent-systems-with-autogen
multiagent-systems-with-autogen
TLDR: This repository contains code examples for building multi-agent applications powered by generative AI models based on the AutoGen framework. It is the official code repository for the book 'Multi-Agent Systems with AutoGen' and provides instructions for setting up Jupyter Notebooks and is organized into chapters with code for various concepts discussed in the book.
oumi
oumi
TLDR: Everything you need to build state-of-the-art foundation models, end-to-end.
awesome-llm-apps
awesome-llm-apps
TLDR: A curated collection of LLM apps built with RAG and AI agents. Features apps using models from various sources and includes projects in different domains. Also provides instructions for getting started and welcomes contributions.
firecrawl
firecrawl
TLDR: Firecrawl is an API service that crawls URLs and converts them into clean markdown or structured data. It offers advanced scraping, crawling, and data extraction capabilities with features like LLM-ready formats, customizability, and more. It also has SDKs for various languages and integrations with multiple frameworks.
RD-Agent
RD-Agent
TLDR: Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through our open source R&D automation tool RD-Agent, which lets AI drive data-driven AI.