---
title: "RAG at Scale: Document Processing, Embeddings, and LLM Generation"
description: "Build enterprise-grade RAG systems that handle millions of documents with automatic chunking, embedding synchronization, and LLM-powered answer generation."
keywords:
  - rag at scale
  - enterprise rag system
  - rag implementation guide
  - production rag pipeline
  - document rag python
complexity: "advanced"
estimated_time: "45 min"
url: "https://pixeltable.com/use-cases/retrieval-augmented-generation-systems"
---

# 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.

## Prerequisites

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

## The Problem

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

### Scalable RAG Foundation

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

```python
import pixeltable as pxt
from pixeltable.functions.document import document_splitter
from pixeltable.functions import openai

# Document store
documents = pxt.create_table('app.rag_docs', {
    'document': pxt.Document,
    'title': pxt.String,
    'source': pxt.String,
})

# Chunking with configurable strategy
chunks = pxt.create_view(
    'app.rag_chunks',
    documents,
    iterator=document_splitter(
        document=documents.document,
        separators='sentence',
        limit=512,
        overlap=50
    )
)

# Embedding with automatic indexing
chunks.add_embedding_index(
    'text',
    string_embed=openai.embeddings.using(
        model='text-embedding-3-small'
    )
)
```

Add documents anytime: chunking, embedding, and indexing happen automatically and incrementally.


### Retrieval + Generation

Chain retrieval to LLM generation with automatic context management.

```python
# Reusable retrieval query
@pxt.query
def retrieve(question: str, n: int = 5):
    return chunks.select(
        chunks.text, chunks.title
    ).order_by(
        chunks.text.similarity(string=question), asc=False
    ).limit(n)

# Queries table with full RAG pipeline
queries = pxt.create_table('app.rag_queries', {
    'question': pxt.String,
})

queries.add_computed_column(
    context=retrieve(queries.question)
)

queries.add_computed_column(
    answer=openai.chat_completions(
        model='gpt-4o',
        messages=[{
            'role': 'system',
            'content': 'Answer based on context. Cite sources.'
        }, {
            'role': 'user',
            'content': queries.question.apply(
                lambda q: f"Context: {queries.context}\nQ: {q}"
            )
        }]
    ).choices[0].message.content
)
```

Insert a question and get a grounded answer automatically. Same question? Cached response, zero API cost.


## 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

## Use Cases

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

## Performance


| Metric | Value | Description |

| --- | --- | --- |

| Development Time | 60% faster | vs building from separate components |

## Requirements

- Python 3.9+
- OpenAI API key
- 16GB+ RAM recommended for large collections

## Resources

- [Production RAG: Data-Centric Approach](https://pixeltable.com/blog/production-rag-data-centric) - Best practices for production RAG
- [Embedding Management Guide](https://pixeltable.com/blog/embedding-management-guide) - Managing embeddings at scale