rag_pipeline

RAG Pipeline Example

This example demonstrates how to build a Retrieval-Augmented Generation (RAG) pipeline using LangGraphGo.

Overview

Shows how to:

• Build document retrieval workflows
• Integrate retrieval with generation
• Manage document embeddings and search
• Create end-to-end RAG systems

Running the Example

cd examples/rag_pipeline
go run main.go

Key Features

• Document Retrieval: Vector-based document search
• Context Integration: Combine retrieved docs with queries
• Generation Pipeline: LLM-based response generation
• Scalable Architecture: Handle large document collections