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Advanced · 45 min

RAG at Scale: Document Processing, Embeddings, and LLM Generation

Build enterprise-grade RAG systems that handle millions of documents with automatic chunking, embedding synchronization, and LLM-powered answer generation.

Quick StartView on GitHubDocumentation

The Challenge

Scaling RAG beyond prototypes requires solving hard problems: chunking strategies that preserve context, embedding models that stay synchronized, vector indexes that update incrementally, and LLM pipelines that handle failures gracefully. Most teams spend months on infrastructure before writing application logic.

The Solution

Pixeltable provides production-ready RAG infrastructure out of the box. document_splitter handles chunking with configurable strategies. Embedding indexes stay synchronized automatically. Computed columns chain retrieval to generation with built-in caching and error handling.

Implementation Guide

Step-by-step walkthrough with code examples

Step 1 of 2

Scalable RAG Foundation

Set up document processing that scales from hundreds to millions of documents.

python
1import pixeltable as pxt
2from pixeltable.functions.document import document_splitter
3from pixeltable.functions import openai
4

5# Document store
6documents = pxt.create_table('app.rag_docs', {
7    'document': pxt.Document,
8    'title': pxt.String,
9    'source': pxt.String,
10})
11

12# Chunking with configurable strategy
13chunks = pxt.create_view(
14    'app.rag_chunks',
15    documents,
16    iterator=document_splitter(
17        document=documents.document,
18        separators='sentence',
19        limit=512,
20        overlap=50
21    )
22)
23

24# Embedding with automatic indexing
25chunks.add_embedding_index(
26    'text',
27    string_embed=openai.embeddings.using(
28        model='text-embedding-3-small'
29    )
30)
Add documents anytime: chunking, embedding, and indexing happen automatically and incrementally.

Key Benefits

Complete RAG pipeline, no separate vector DB or orchestrator
Automatic embedding synchronization on document changes
Built-in caching reduces LLM costs dramatically
Scales from prototype to millions of documents
Full traceability: every answer links to its source chunks

Real Applications

Enterprise knowledge management
Customer support with grounded answers
Legal document analysis and research
Academic research question-answering

Prerequisites

Understanding of LLMs and embeddings
Experience with document processing
Python and API integration
Python 3.9+
OpenAI API key
16GB+ RAM recommended for large collections

Performance

Development Time
vs building from separate components
60% faster

Learn More

Production RAG: Data-Centric Approach

Best practices for production RAG

Embedding Management Guide

Managing embeddings at scale

Related Guides

Ready to get started?

Install Pixeltable and start building in minutes. One pip install, no infrastructure to manage.

View on GitHubQuick Start GuideStarter Kit