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Intermediate · 30 min

Production RAG: From Documents to Answers in One System

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

Quick StartView on GitHubDocumentation

The Challenge

Production RAG systems require coordinating document processing, chunking strategies, embedding generation, vector storage, retrieval optimization, and LLM integration. Most teams end up stitching together 5+ tools: a document parser, a chunker, an embedding API, a vector DB, and an LLM framework, each with its own failure modes.

The Solution

Pixeltable unifies the entire RAG stack into one declarative system. Documents are ingested as native types, automatically chunked via views, embedded via computed columns, and indexed for retrieval. LLM generation is just another computed column. Everything stays in sync automatically.

Implementation Guide

Step-by-step walkthrough with code examples

Step 1 of 4

Ingest Documents

Create a table for your knowledge base and insert documents.

python
1import pixeltable as pxt
2

3# Create document store
4docs = pxt.create_table('app.documents', {
5    'document': pxt.Document,
6    'title': pxt.String,
7    'source': pxt.String,
8    'doc_type': pxt.String,
9})
10

11# Insert documents: PDF, DOCX, HTML, Markdown
12docs.insert([
13    {'document': '/data/product_guide.pdf',
14     'title': 'Product Guide v3', 'source': 'internal', 'doc_type': 'pdf'},
15    {'document': 'https://example.com/api-docs.html',
16     'title': 'API Reference', 'source': 'docs-site', 'doc_type': 'html'},
17])
Pixeltable handles PDF extraction, HTML parsing, and text extraction automatically via the Document type.

Key Benefits

Complete RAG stack in one system, no vector DB, no orchestrator
Automatic embedding synchronization when documents change
Built-in caching reduces LLM API costs by up to 60%
Incremental updates: add a document and only its chunks are processed
Full lineage: trace any answer back to its source chunks and documents

Real Applications

Enterprise knowledge bases and internal search
Customer support chatbots with grounded answers
Research question-answering over large document collections
Legal and compliance document analysis
Product documentation assistants

Prerequisites

Understanding of vector embeddings and LLMs
Basic Python and API integration experience
Python 3.9+
OpenAI API key for embeddings and LLM generation
8GB+ RAM recommended for large document collections

Performance

Development Time
vs building from separate components
10x faster
API Cost Savings
With built-in caching and deduplication
60%

Learn More

AI Automation Workflow: The Pipeline Is the Table

Document RAG as a table, not a vector-DB stitch

Production RAG: Data-Centric Approach

Building reliable RAG with data-centric principles

Multimodal RAG: Dev to Production

End-to-end multimodal RAG deployment guide

Embedding Management Guide

Production-ready embedding management patterns

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