RAGs lets you build your own "ChatGPT over data" in 3 steps: describe your task and data, configure parameters like top-k retrieval, then query your data via convo with the auto-generated RAG agent.
RAGs is an open source Streamlit application that allows you to create your own Retrieve and Generate (RAG) pipeline to query over your data through natural language conversations.
-
1️⃣ Natural language setup: Describe your dataset and desired task in plain English to initialize a RAG system. No coding needed.
-
2️⃣ Customizable architecture: Tailor parameters like top-k retrieval, summarization options, choice of language model, and more through an intuitive UI.
-
3️⃣ Conversational interaction: Once configured, engage in a chatbot-style dialogue where the RAG agent answers your natural language questions by selecting and applying the right tools to your data.
So in just 3 steps, RAGs allows you to go from idea to implementation of a production-grade RAG system fine-tuned to your use case, no coding required. It democratizes access to this advanced NLP technique and makes querying data through conversation available to all.
The goal is to enable "ChatGPT over your data" for everyone.
- ⚡️ Fast testing: RAGs provides the ability to swiftly try out RAG concepts and proofs of concept without needing to code an entire pipeline. The straightforward configuration through natural language facilitates exploration.
- ⚙️ Adjustability: AI developers can really customize RAG parameters such as lookup, summarization, embedding models, LLMs, etc. This adaptability gives fine-grained control to construct the optimal architecture.
- 📈 Scalability: Since RAGs handles the underlying pipeline building blocks, engineers can concentrate on higher-level challenges like scaling, deployment, and integration. Constructing custom frontends is also simpler.
- 🧑🏫 Learning: For AI engineers looking to gain knowledge, Rags exposes all key RAG components through an intuitive interface. Great for hands-on testing and adjustment.
- 🤝 Open source community: As an open source project, Rags provides opportunities to cooperate, share ideas/code, and interact with an active community progressing the state of conversational AI.
In summary, RAGs artfully blends ease of use with advanced capabilities for swiftly building bespoke RAG systems. It streamlines both mocking up proofs of concept and deploying highly customized solutions, making it an indispensable asset for AI engineers operating on the frontier of natural language processing. Whether iterating new ideas or actualizing specialized designs, RAGs empowers progress at the forefront.
- 👷🏽♀️ Builders: Jerry Liu, Logan Markewich, Alexandros Filothodoros
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/jerry-liu-64390071/, https://www.linkedin.com/in/logan-markewich/, https://www.linkedin.com/in/alfilo/
- 👩🏽🏭 Builders on X: https://twitter.com/jerryjliu0, https://twitter.com/LoganMarkewich, https://twitter.com/eltociear
- 👩🏽💻 Contributors: 5
- 💫 GitHub Stars: 4.9k
- 🍴 Forks: 571
- 👁️ Watch: 39
- 🪪 License: MIT
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/run-llama/rags
- Official Website: https://blog.llamaindex.ai/introducing-rags-your-personalized-chatgpt-experience-over-your-data-2b9d140769b1
- LinkedIn Page: https://www.linkedin.com/company/llamaindex/about/
- X Page: https://twitter.com/llama_index
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/rags.md
🧙🏽 Follow The AI Engineer for more about rags and daily insights tailored to AI engineers. Subscribe to our newsletter. We are the AI community for hackers!
♻️ Repost this to help rags become more popular. Support AI Open-Source Libraries!