RAG pipeline

A Retrieval-Augmented Generation (RAG) pipeline enhances Large Language Model (LLM) outputs by fetching relevant data from external sources, reducing hallucinations and improving accuracy. It involves indexing documents into a vector database, retrieving relevant chunks based on user queries, and augmenting the prompt with this context to generate grounded responses.

How a RAG Pipeline Works

The RAG pipeline operates through two main phases: ingestion (preparing knowledge) and retrieval (generating answers). 

1. Data Ingestion & Indexing: Documents (PDFs, websites, databases) are loaded, split into smaller chunks, converted into numerical embeddings via an AI model, and stored in a vector database.
2. Retrieval & Generation: A user query is converted into a vector and matched against the vector database to find the most relevant chunks. These chunks, along with the original question, are fed to the LLM to generate a precise answer. 

cit.: YouTube

Benefits of RAG Pipelines

• Reduced Hallucinations: Grounding answers in provided documents ensures higher factual accuracy.
• Up-to-Date Information: Enables models to access, for example, the latest internal documents without retraining.
• Data Privacy: Allows querying private or proprietary data sources securely. 

cit.: NVIDIA Developer

How to Build a RAG Pipeline

Building a RAG pipeline typically involves these steps:

1. Load Data: Use loaders for files, websites, or databases.
2. Chunking: Divide data into smaller, manageable text sections.
3. Embeddings & Storage: Use embedding models to vectorize text and store them in a vector store like Pinecone, ChromaDB, or Weaviate.
4. Retrieval Engine: Build a mechanism to perform semantic searches for relevant info.
5. LLM Generation: Send retrieved content to a model (like GPT-4) to generate the answer.
6. Frameworks: Utilize frameworks like LangChain or LlamaIndex to connect these components. 

cit.: YouTube

source: AI summary

reference: https://developer.nvidia.com/blog/rag-101-demystifying-retrieval-augmented-generation-pipelines/