#jupyter_notebook #agents #artificial_intelligence #generative_ai #llms #rag

This repository helps you learn and build Generative AI applications using MongoDB. It includes many examples and sample apps for different AI tasks, such as Retrieval-Augmented Generation (RAG) and AI Agents. You can find Jupyter notebooks, JavaScript and Python apps, and contributions from AI partners. To get started, you need to create a free MongoDB Atlas account, set up a database cluster, and get the connection string. This resource benefits you by providing step-by-step guides and support, making it easier to integrate MongoDB into your AI projects and learn from community resources.

https://github.com/mongodb-developer/GenAI-Showcase

#jupyter_notebook #agents #ai #genai #langchain #langgraph #llm #llms #openai #tutorials

This repository offers a comprehensive collection of tutorials and implementations for building Generative AI (GenAI) agents. It helps users learn how to create simple conversational bots to complex multi-agent systems. By using this resource, you can improve your skills in developing AI solutions that automate tasks, enhance decision-making, and provide personalized experiences. The benefits include increased efficiency, better customer interactions, and the ability to innovate faster than competitors.

https://github.com/NirDiamant/GenAI_Agents

#jupyter_notebook #ai #langchain #llama_index #llm #llms #opeani #python #rag #tutorials

This project is about improving Retrieval-Augmented Generation (RAG) systems, which combine information retrieval with AI to generate more accurate and relevant responses. By sponsoring this project through GitHub Sponsors, you help support the development of these advanced techniques. Your sponsorship fuels innovation in RAG technologies, allowing for better maintenance and expansion of this valuable resource. This benefits users by providing them with cutting-edge tools and insights that enhance their work with AI systems.

https://github.com/NirDiamant/RAG_Techniques

#python #chatgpt #llms #pyqt #wechat

This tool helps you manage your WeChat data by letting you export and analyze your chat history. It supports WeChat 4.0 and allows you to restore chat interfaces, export data in various formats like HTML, CSV, and Word, and even create visual reports. This means you can keep track of your conversations and memories easily, making it a useful tool for organizing your digital life.

https://github.com/LC044/WeChatMsg

#python #agents #graph #llms #rag

Graphiti helps AI systems handle constantly changing information by building real-time knowledge graphs that track relationships and historical data, allowing them to integrate user interactions, business data, and external sources seamlessly. Unlike traditional methods, it updates information instantly without needing full recomputations, enabling precise historical queries and efficient hybrid searches. This helps AI applications stay context-aware, automate tasks effectively, and manage complex, evolving data with minimal delay.

https://github.com/getzep/graphiti

#typescript #electron #llama #llms #lora #mlx #rlhf #transformers

Transformer Lab is a free, open-source tool that lets you easily work with large language models on your own computer, offering one-click downloads for popular models like Llama3 and Mistral, fine-tuning across different hardware (including Apple Silicon and GPUs), and features like chatting, training, and evaluating models through a simple interface—saving you from complex setups like CUDA or Python version issues[1][2][5].

https://github.com/transformerlab/transformerlab-app

#python #agents #ai #ai_agents #llm #llms #mcp #model_context_protocol #python

The Model Context Protocol (MCP) is a standard way for AI agents to connect with different tools and data sources, making it much easier to build powerful AI applications without writing custom code for each integration[2][5]. The mcp-agent framework uses MCP to let you quickly create agents that can do things like read files, fetch web pages, or manage emails, and you can combine these agents in flexible ways to handle complex tasks. This means you can focus on what you want your AI to do, while mcp-agent takes care of connecting to the right tools and managing the workflow, saving you time and effort[3][5].

https://github.com/lastmile-ai/mcp-agent

#python #agents #document_search #evaluation #guardrails #llms #optimization #prompts #rag #vector_stores

Ragbits is a tool that helps build and deploy GenAI applications quickly. It offers features like swapping between many language models, ensuring safe interactions with these models, and connecting to various data storage systems. Ragbits also includes tools for managing data and testing prompts, making it easier to develop reliable AI applications. This helps users create more accurate and efficient AI systems by integrating the latest data and reducing errors. Overall, Ragbits makes it faster and more efficient to develop and deploy AI applications.

https://github.com/deepsense-ai/ragbits

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#jupyter_notebook #ai #llm #llms #multi_modal #openai #python #rag

Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.

https://github.com/FareedKhan-dev/all-rag-techniques