Daily Dose of Data Science | Avi Chawla | Substack·1yBuilding a 100% Local mini-ChatGPT
A guide on building a local mini-ChatGPT app using the Llama3.2-vision model and Chainlit. The post includes a demo, necessary tools, and step-by-step coding instructions with multimodal prompting. The code and further resources for AI engineering are provided on GitHub.
Daily Dose of Data Science | Avi Chawla | Substack·1yRAG vs Agentic RAG
Agentic RAG systems introduce dynamic, adaptable behaviors into the traditional RAG workflow. Unlike traditional RAG, which retrieves and generates once, agentic RAGs iteratively refine queries and context, adapting based on the problem's complexity. This makes them more effective for complex queries and problem-solving. The open-source tool Opik by CometML supports the evaluation, testing, and monitoring of LLM applications from development to production, offering features like logging traces and detecting hallucinations.
Daily Dose of Data Science | Avi Chawla | Substack·1yA crash course on RAG systems—Part 5
Part 5 of the RAG crash course focuses on the implementation of key components for multimodal RAG systems, such as CLIP embeddings, multimodal prompting, and tool calling. The series aims to educate readers on building reliable RAG systems that can reduce costs and handle complex data types, ultimately aiding businesses in achieving greater impact.
Daily Dose of Data Science | Avi Chawla | Substack·1yA crash course on RAG systems—Part 6
Part 6 of the crash course on RAG systems explores how to build a more extensive and capable multimodal RAG system using CLIP embeddings, multimodal prompting, and tool calling. The post includes a unique dataset combining social media posts with images to provide a practical learning experience. The series covers everything from foundational components and evaluation to optimization and handling complex documents, aiming to help users implement reliable RAG systems and solve key NLP challenges with LLMs.
Daily Dose of Data Science | Avi Chawla | Substack·1yBuilding a RAG app using Llama-3.3
Meta released Llama-3.3, and this post provides a hands-on demo for building a RAG app using it. The app allows users to interact with a document via chat. It uses LlamaIndex for orchestration, Qdrant for a self-hosted vector database, and Ollama for serving Llama-3.3 locally. The implementation steps include loading and parsing a knowledge base, creating embeddings, indexing and storing them, defining a custom prompt template, and setting up a query engine.