rag-demystified
autogen
| rag-demystified | autogen | |
|---|---|---|
| 3 | 34 | |
| 754 | 28,000 | |
| - | 5.7% | |
| 8.2 | 9.9 | |
| 5 months ago | 1 day ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Creative Commons Attribution 4.0 |
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rag-demystified
- Show HN: Demystifying Advanced RAG Pipelines
- Show HN: Demystifying Advanced Rag Pipelines
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Good LLM Validation Is Just Good Validation
This was exactly my experience trying to write a RAG pipeline that answers complex questions by generating sub-questions [1]. The hidden prompts in the complex libraries are quite difficult to get to and then start tweaking, whereas writing my own pipeline boiled down the code to 4 LLM calls.
[1] https://github.com/pchunduri6/rag-demystified
autogen
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How Generative AI is impacting Developer Productivity?
AutoGen
- Multi AI Agent Systems using OpenAI's new GPT-4o Model
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Agents of Change: Navigating the Rise of AI Agents in 2024
AutoGen is an AI framework by Microsoft designed to streamline multi-agent conversations. AutoGen allows agents to communicate, share information, and make collective decisions. This setup enhances the responsiveness and dynamism of conversations. Developers use AutoGen to tailor agents to specific roles, such as programmer, content writer, CEO, etc. This enhances their ability to handle tasks from simple queries to intricate problem-solving.
- FLaNK AI Weekly 25 March 2025
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Launch HN: Glide (YC W19) – AI-assisted technical design docs
I am still playing around with the project but FYI, the parsing for the github repo URL at https://glide.agenticlabs.com/ will fail if there's a trailing slash in the repo link i.e. https://github.com/microsoft/autogen/ won't work but https://github.com/microsoft/autogen will.
- Show HN: Prompts as (WASM) Programs
- Enable Next-Gen Large Language
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AutoGen v0.2.2 released
New example notebook demoing video transcript translate with whisper.
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AutoGen v0.2.1 released
New release: v0.2.1
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AI is making us all more productive — but in a weird and unexpected way
I disagree with the conclusion. In software, I've seen 10x engineers in person and I don't think they're replaceable. Whereas, the new college grad or that entry level dev who doesn't design anything and just writes small amounts of code, doing exactly as told is replaceable by an AI. Frameworks similar to Microsoft Autogen(https://github.com/microsoft/autogen) can in theory build agents who can do these tasks with ease whereas a 10x engineer can focus on directing the agents and designing systems.
What are some alternatives?
waggle-dance - Knowledge work automation with AI agents
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
TypeChat - TypeChat is a library that makes it easy to build natural language interfaces using types.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
AgentVerse - 🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation
langchain - 🦜🔗 Build context-aware reasoning applications
langroid - Harness LLMs with Multi-Agent Programming
awesome-ai-agents - A list of AI autonomous agents
add-gpt-chatbot-to-microsoft-word - Create a chatbot in Microsoft Word powered by ChatGPT
awesome-assistant-api - Try openai assistant api apps on Google Colab for free. Awesome assistant API Demos!