Building a Basic RAG Pipeline
Having established the foundational concepts of RAG, including its architecture, retrieval methods, data preparation techniques, and the generation process in the preceding chapters, we now shift focus to practical application. This chapter guides you through the assembly of a basic, end-to-end Retrieve-Augmented Generation pipeline.
You will learn the essential steps involved in putting the pieces together:
- Setting up a suitable development environment with the required libraries.
- Implementing the retriever component, connecting to a vector store, and performing similarity searches.
- Integrating a Large Language Model (LLM) to act as the generator.
- Combining the retriever and generator into a cohesive sequence or chain.
- Briefly reviewing frameworks (like LangChain or LlamaIndex) that streamline RAG development.
- Executing queries against the constructed pipeline to observe its operation.
By the end of this chapter, you will have built and tested a functional RAG system using standard tools and techniques.
Sections
5.1 Overview of RAG Frameworks (e.g., LangChain, LlamaIndex)
5.2 Setting up the Environment
5.3 Implementing the Retriever
5.4 Implementing the Generator Integration
5.5 Combining Retrieval and Generation
5.6 Running Queries Through the Pipeline
5.7 Hands-on Practical: End-to-End RAG System
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