Michael Becker

Introducing RAGApp 💫 A no-code interface to configure a RAG chatbot, as dead-simple as GPTs by @OpenAI. It’s a docker container that’s easily deployable in any cloud infrastructure. Best of all, it’s fully open-source 🔥 1️⃣ Setup the LLM: Configure the model provider (OpenAI, Gemini) 2️⃣ Setup the data: Define the system prompt and upload your knowledge base. 3️⃣ Launch the chatbot both via the UI or API 4️⃣ If via the UI, stream intermediate events and also sources! This is fantastic work by @MarcusSchiesser and is built upon the same DNA as our create-llama project. Check out RAGApp today: github.com/ragapp/ragapp

RAFT - Retrieval Augmented Fine Tuning 🔥 RAFT offers a method to fine-tune pre-trained LLMs for specific domain RAG settings. Conventional RAG is like an open-book exam, retrieving documents from an index to provide context for answering queries. This makes it more effective than the closed-book exam setting where LLMs rely solely on their pre-training and fine-tuning to respond to prompts, but doesn't allow the LLM to learn the domain beforehand. RAFT from @tianjun_zhang, @shishirpatil_ proposed a fine-tuning approach that enhances LLMs for domain-specific open-book exams, where the model is trained to attend to relevant docs and ignore irrelevant documents. RAFT creates a synthetic dataset where each data sample consists of: 1️⃣ A question 2️⃣ Two docs: An "oracle" document relevant to the question, and a "distractor" documents irrelevant to the question. 3️⃣ An answer generated from the documents 4️⃣ A Chain-of-Thought explanation including excerpts from the relevant documents. The created synthetic dataset is further used for fine-tuning to improve RAG performance. Thanks to @ravithejads, you can now generate RAFT dataset using our brand new RAFTDatasetPack LlamaPack. LlamaPack: github.com/run-llama/llama_i… Video Tutorial: youtube.com/watch?v=sqPckknl… Paper: arxiv.org/abs/2403.10131