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YouTube

Agentic RAG: Implementing Document Retrieval with SmolAgents - Three Key Lessons

Trelis Research via YouTube

Overview

Learn how to implement agentic RAG (Retrieval-Augmented Generation) systems through a comprehensive 54-minute video tutorial that explores three key insights about function calling and embeddings. Master document retrieval techniques using SmolAgents, starting with basic implementation in HuggingFace's safe Python environment and progressing through document preparation, Markdown conversion, and table of contents generation. Dive into BM25 retrieval tool implementation, understand section retrieval mechanisms, and explore the integration of section reading tools. Follow along with practical demonstrations using the Claude model and various language models, while learning setup instructions and code execution techniques. Gain valuable insights into context management considerations and future improvement areas for building more effective document retrieval systems.

Syllabus

- Introduction to using agents for document retrieval
- Three key insights about function calling and embeddings
- Explanation of traditional function calling approach
- Introduction to SmolAgents and code execution
- Discussion of HuggingFace's safe Python environment
- Demo of SmolAgents basic implementation
- Setup instructions for running the code
- Explanation of document preparation and Markdown conversion
- Discussion of table of contents generation
- Introduction to BM25 retrieval tool
- Implementation details of BM25 search
- Setup of document section reading tool
- Explanation of section retrieval implementation
- Demo of BM25 retriever tool
- Integration of section reading tool
- Demonstration of combined tools with Claude model
- Testing with different language models
- Summary and conclusion of tutorial
- Mention of future improvements needed for context management

Taught by

Trelis Research

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