Integrating All RAG Components
Assemble document loaders, embeddings, vector stores, and LLMs into a cohesive LangChain RAG application.
Welcome to RAG Integration
Welcome to this lesson on integrating all the components of a Retrieval Augmented Generation (RAG) system using LangChain!
So far, you've learned about individual pieces: document loaders, text splitters, embedding models, vector stores, LLMs, and prompts. Now, it's time to bring them all together.
The RAG Pipeline Flow
A RAG application follows a clear pipeline to answer questions using external knowledge. It generally involves these steps:
- Load: Ingest data from various sources.
- Split: Break documents into manageable chunks.
- Embed: Convert text chunks into numerical vectors.
- Store: Save these vectors in a searchable database.
- Retrieve: Find relevant chunks based on a user query.
- Generate: Use an LLM to answer the query, referencing the retrieved context.
All lessons in this course
- Integrating All RAG Components
- Querying and Generating Answers
- Evaluating RAG System Performance
- Building a Golden Test Set for RAG