Building Production RAG Systems: Lessons from StructureGPT
•1 min read
RAGLLMProduction AIPython
## Introduction
When building StructureGPT, a RAG system for UK Building Regulations compliance, I learned that production AI systems require much more than just connecting an LLM to a vector database. Here's what I discovered.
## Architecture Overview
The system consists of three main components:
1. **Document Processing Pipeline**: Chunking and embedding building regulations
2. **Retrieval System**: ChromaDB for similarity search
3. **Generation Layer**: Fine-tuned LLaMA-3.1-8b with LoRA
```python
from langchain.embeddings import HuggingFaceEmbeddings
from chromadb import Client
# Initialize embeddings
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
# Setup vector store
client = Client()
collection = client.create_collection("building_regs")