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Build Retrieval-Augmented Generation (RAG) Systems at Scale

Complete guide to building production RAG systems. Handle document processing, embedding management, and LLM integration.

Docs

Challenge

Building RAG systems requires coordinating document processing, chunking strategies, embedding generation, vector storage, retrieval optimization, and LLM integration across multiple systems.

Solution

Pixeltable unifies the entire RAG stack. From document ingestion to LLM generation, everything is managed through declarative computed columns with automatic synchronization.

Implementation Steps

Step 1 of 2

Set up document processing and embedding generation

import pixeltable as pxt
from pixeltable.iterators import DocumentSplitter
from pixeltable.functions import openai


# Document ingestion and chunking
documents = pxt.create_table('rag_documents', {
    'document': pxt.Document,
    'title': pxt.String,
    'source': pxt.String
})


# Automatic chunking with overlaps
chunks = pxt.create_view(
    'document_chunks',
    documents,
    iterator=DocumentSplitter.create(
        document=documents.document,
        chunk_size=512,
        chunk_overlap=50
    )
)


# Automatic embedding generation
chunks.add_computed_column(
    embedding=openai.embeddings(
        chunks.text,
        model='text-embedding-3-large'
    )
)


# Automatic vector indexing
chunks.add_embedding_index('text', embedding=chunks.embedding)

💡 Complete RAG foundation with automatic document processing and indexing.

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Key Benefits

Complete RAG stack in one system
Automatic embedding synchronization
60% faster RAG development
Built-in retrieval optimization
Production-ready scalability

Real Applications

•Enterprise knowledge bases
•Customer support chatbots
•Research question answering
•Document intelligence platforms

Prerequisites

•Understanding of LLMs and embeddings
•Experience with document processing
•Python and API integration knowledge

Technical Needs

•Python 3.9+
•OpenAI API key
•Document storage (local or cloud)

Performance

Development Time
vs building from scratch
60% faster

Learn More

Production RAG: Data-Centric ApproachMultimodal RAG Production GuideEmbedding Management Guide

Related Guides

How to Implement RAG in Production: Complete Python GuideDeclarative AI Data Infrastructure: Build Production Pipelines Without CodeMultimodal AI Application Development: Build Apps That Process Any Data Type

Ready to Get Started?

Install Pixeltable and build your own build retrieval-augmented generation (rag) systems at scale in minutes.

View on GitHubQuick Start Guide