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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 — no vector database or orchestrator required.

Quick Start View on GitHub Documentation

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.

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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

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

Build an end-to-end video analysis system with Pixeltable. Ingest video, extract frames, run multimodal AI models, generate embeddings, and enable semantic search — all as computed columns on a table.

Computer Vision Pipeline: Object Detection, Classification, and Search

Build optimized computer vision workflows with Pixeltable. Run YOLOX, CLIP, and custom models as computed columns with automatic batching, caching, and incremental processing.

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.

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