Integrating with LLM Frameworks
Learn to connect vector databases with popular LLM orchestration frameworks like LangChain or LlamaIndex.
LLM Frameworks for RAG
Welcome! In this lesson, we'll learn how to connect vector databases with powerful LLM orchestration frameworks. These frameworks simplify building complex AI applications like Retrieval-Augmented Generation (RAG).
Think of them as tools that help your language model talk to your vector database efficiently.
Why Use LLM Frameworks?
Building RAG applications involves many steps: loading data, splitting text, generating embeddings, storing them in a vector database, retrieving relevant chunks, and finally, feeding them to an LLM.
LLM frameworks streamline this process by:
- Abstracting complexity: Providing a unified interface for various components.
- Modularity: Allowing you to easily swap out different models or databases.
- Workflow management: Helping chain together different operations.
All lessons in this course
- RAG System Architecture Overview
- Integrating with LLM Frameworks
- Contextual Information Retrieval
- Chunking Strategies for RAG