RAG Systems, Embeddings, and LangChain Framework 2025 07 13

This video covers a detailed discussion on Retrieval-Augmented Generation (RAG) systems, starting from basic concepts of external document processing, embedding models like all-MiniLM-L6-V2 and Qwen, and vector databases. It demonstrates a practical example of building a RAG system using the "Alice in Wonderland" book as a corpus and explores the challenges of converting PDFs to text. The session then transitions into the LangChain framework, explaining its role in simplifying complex AI problems, model loading, prompt engineering, and utilizing various tools and agents. The video also includes a demonstration of a LangChain-based RAG application with the "Game of Thrones" book.

Timestamps:

* 00:00:00 - Introduction to RAG Systems and Embedding Models
* 00:02:16 - Discussion on All-MiniLM-L6-V2 and Qwen Embeddings
* 00:03:56 - Benchmarking and Evaluation of Models
* 00:05:11 - Smaller Embeddings for Edge Devices and Model Sizes
* 00:06:49 - Transition to Agentic Use Cases and AI Model Overview
* 00:08:15 - Deep Dive into Text to Embedding Conversion and Vector Databases
* 00:09:15 - Cosine Similarity for Document Retrieval
* 00:10:23 - Ensuring LLM Accuracy and Avoiding Hallucination
* 00:11:27 - Practical RAG Demonstration with "Alice in Wonderland"
* 00:13:23 - PDF to Text Conversion and Docling Library
* 00:16:28 - Loading Models and Setting Up the Pipeline
* 00:18:42 - Base Models vs. Instruct Models
* 00:20:44 - Loading Tokenizer and Model for Experimentation
* 00:21:49 - Discussion on Running Models Locally vs. Colab
* 00:25:07 - Setting Up Local Environments with Docker
* 00:26:05 - Chunking and Storing Documents as Embeddings
* 00:31:19 - Preparing Document Embeddings and Vector DB Storage
* 00:34:04 - Querying the RAG System and Generating Answers
* 00:37:07 - Analyzing RAG System's Answer Quality
* 00:38:56 - RAG System Workflow and Components
* 00:40:50 - LangChain Introduction: Beyond Basic Models
* 00:42:38 - The Need for LangChain: Autosolving Complex Problems
* 00:44:00 - LLM Integration with External Tools and APIs
* 00:47:39 - Example: Supply Chain Optimization with LLMs and Tools
* 00:50:49 - Agents, Multiple Agents, and Automated Reasoning
* 00:52:11 - LangChain's Role in Prompt Building and RAG Architectures
* 00:53:50 - Simplifying Model Loading with LangChain
* 00:55:01 - Advanced Prompting Techniques: Examples and Steps
* 00:56:30 - Fine-tuning and Encoder Models for Improved Performance
* 00:57:59 - Chains and Retrieval in LangChain
* 00:59:21 - Utilizing Tools and Memory in LangChain
* 01:00:43 - LangGraph for Graph Data Structures
* 01:01:51 - LangChain Platform for Tracing and Monitoring
* 01:03:06 - Optimizing and Evaluating RAG Systems
* 01:04:21 - Class Schedule and Exercise Overview
* 01:05:49 - Installing LangChain Libraries and Llama.cpp Python
* 01:07:11 - Llama.cpp and GGUF Format for CPU Optimization
* 01:09:51 - Loading Qwen GGUF Model for Demonstration
* 01:18:50 - Quantization and Model Size Impact
* 01:21:49 - Max Tokens and Temperature in Model Generation
* 01:23:23 - Simple LLM Invocation with LangChain
* 01:25:42 - Introducing Chat Prompt Template
* 01:27:27 - Building Prompt Templates for AI Assistants
* 01:29:45 - Simple Chain with Runnable Pass Through
* 01:31:37 - Running a Simple Chain with a Prompt
* 01:38:27 - Troubleshooting and Running the Chain
* 01:40:40 - Analyzing Model's Response to Complex Questions
* 01:42:47 - Refining Prompts and Addressing Hallucination
* 01:46:29 - Trying a Larger Model for Better Accuracy
* 01:51:15 - Setting Up RAG Application with Game of Thrones Book
* 01:54:33 - Loading and Splitting Documents in LangChain
* 01:58:10 - Loading Embeddings and Chroma DB
* 02:01:34 - Creating Retriever and Prompt Template
* 02:05:56 - Running the RAG Application with Game of Thrones
* 02:10:45 - Evaluating the RAG System's Performance
* 02:17:50 - LangChain Document Loaders and Future Steps
* 02:19:37 - Final Answer Analysis and Next Steps

Видео RAG Systems, Embeddings, and LangChain Framework 2025 07 13 канала InventzAI (AI for Everyone)