RAG Template FAQs

Question 1

What is the RAG Template?

Accepted Answer

The RAG Template is a production-ready project template for building Retrieval-Augmented Generation (RAG) pipelines. It integrates FastMCP, PostgreSQL with pgvector, and local embedding generation to streamline data ingestion and semantic querying for LLM applications.

Question 2

What core technologies does RAG Template leverage?

Accepted Answer

It leverages FastMCP for LLM client integration, PostgreSQL with pgvector for efficient vector similarity search and storage, and sentence-transformers for local embedding generation.

Question 3

How does the RAG Template process data?

Accepted Answer

The template features an automated pipeline that takes CSV input, cleans the data, performs configurable text chunking with overlap, generates local embeddings, and stores them in PostgreSQL with pgvector for semantic retrieval.

Question 4

Is the RAG pipeline configurable?

Accepted Answer

Yes, key aspects like text chunk size, chunk overlap, and the embedding model (e.g., all-MiniLM-L6-v2) are configurable via environment variables, allowing for flexible optimization.

Question 5

What are the main benefits of using RAG Template?

Accepted Answer

RAG Template accelerates the development of robust RAG applications by providing a pre-built, production-ready stack for data ingestion, semantic search, and LLM context provisioning, simplifying complex infrastructure setup.

RAG Template

RAG Template

Key Features

Use Cases

Key Features

Use Cases