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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 Start View on GitHub Documentation

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. DocumentSplitter 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.iterators import DocumentSplitter
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=DocumentSplitter.create(
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.

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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

Build a complete Retrieval-Augmented Generation pipeline with Pixeltable. Ingest documents, chunk text, generate embeddings, index for retrieval, and generate LLM answers — no vector database or orchestrator required.

AI Agents & MCP: Give Your Agents Persistent Multimodal Memory

Build AI agents with durable memory and tool-calling capabilities using Pixeltable and Model Context Protocol (MCP). Store conversations, images, and documents as queryable tables that agents can read from and write to.

Declarative AI Infrastructure: Define Pipelines, Not Plumbing

Replace thousands of lines of orchestration code with declarative computed columns. Pixeltable handles execution, dependencies, caching, and incremental updates automatically.

Ready to Get Started?

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

View on GitHub Quick Start Guide Starter Kit