Curriculum
- 5 Sections
- 38 Lessons
- 14 Hours
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- Introduction to RAG and the Toolset9
- 1.1Introduction to the RAG concept
- 1.2Presentation of the used technologies: LangChain, LangGraph, ChromaDB, Streamlit, and LLMs API
- 1.3Setting up the development environment
- 1.4Understanding the components of a RAG system: Retriever and Generator
- 1.5Discussing different retrieval strategies
- 1.6Exploring the role of embeddings and vector databases
- 1.7Loading and processing documents
- 1.8Splitting documents into smaller chunks for effective retrieval
- 1.9Generating document embeddings using various embedding models
- Building the Retrieval System with ChromaDB9
- 2.1Concepts of vector embeddings and similarity search
- 2.2Introduction to ChromaDB as a persistent vector store
- 2.3Setting up and initializing a ChromaDB collection
- 2.4Loading documents and creating embeddings using LangChain
- 2.5Storing document chunks and their embeddings in ChromaDB
- 2.6Discussing strategies for handling large datasets
- 2.7Querying the ChromaDB index for relevant document chunks based on user input
- 2.8Exploring different similarity search methods provided by ChromaDB
- 2.9Integrating ChromaDB with LangChain’s retriever interface
- Orchestrating the RAG Agent with LangGraph9
- 3.1The need for state management and orchestration in complex LLM applications
- 3.2Presentation of LangGraph’s state-based approach and graph representation
- 3.3Defining states and nodes in a LangGraph application
- 3.4Creating nodes for document retrieval and LLM generation
- 3.5Running a simple RAG query through the LangGraph
- 3.6Managing conversation history within the LangGraph state
- 3.7Handling multi-turn conversations
- 3.8Introducing the concept of tools for LLM agents
- 3.9Enable the agent to interact with external systems
- Building the User Interface with Streamlit6
- 4.1Creating simple Streamlit applications
- 4.2Integrating the LangGraph agent execution within a Streamlit application
- 4.3Passing user input from Streamlit to the LangGraph
- 4.4Displaying the agent’s responses in the Streamlit interface
- 4.5Displaying source documents for retrieved information
- 4.6Creating a conversational chat interface
- Advanced Topics and Evaluation5
- 5.1Discussing techniques like HyDE, re-ranking, and query expansion
- 5.2Considering different LLM prompting strategies for RAG
- 5.3Metrics for evaluating retrieval quality and generation quality
- 5.4Discussing methods for testing and debugging RAG agents
- 5.5Options for deploying Streamlit applications and the RAG agent
Presentation of the used technologies: LangChain, LangGraph, ChromaDB, Streamlit, and LLMs API
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