Large Language Models (LLMs) have revolutionized how we build AI applications, with Retrieval-Augmented Generation (RAG) emerging as a key pattern for enhancing LLM responses with domain-specific knowledge. However, as RAG applications move from proof-of-concept to production, developers face significant infrastructure challenges. In this post, we’ll explore how Pixeltable’s declarative infrastructure simplifies RAG development while providing production-ready features out of the box.
Common RAG Development Challenges
Building production RAG applications involves several complex tasks:
- Managing and updating document collections efficiently
- Experimenting with different chunking strategies
- Maintaining embedding indexes
- Ensuring result reproducibility
- Tracing LLM outputs back to source documents
Traditional approaches often involve cobbling together multiple tools and writing custom pipeline code, leading to maintenance headaches and scaling issues.
Enter Pixeltable: Declarative RAG Infrastructure
Pixeltable reimagines RAG development with a declarative, table-based approach. Here’s a complete RAG pipeline in just a few lines of code:
import pixeltable as pxt
from pixeltable.iterators import DocumentSplitter
from pixeltable.functions.huggingface import sentence_transformer
# Create base table for documents
docs = pxt.create_table('knowledge_base', {
'document': pxt.DocumentType(),
'metadata': pxt.JsonType()
})
# Create view for document chunks
chunks = pxt.create_view(
'chunks',
docs,
iterator=DocumentSplitter.create(
document=docs.document,
separators='token_limit',
limit=300
)
)
# Add embeddings and create search index
chunks.add_embedding_index('text', string_embed=e5_embed)
query_text = "What is the expected EPS for Nvidia in Q1 2026?"
sim = chunks_t.text.similarity(query_text)
nvidia_eps_query = (
chunks_t
.order_by(sim, asc=False)
.select(similarity=sim, text=chunks_t.text)
.limit(5)
)
nvidia_eps_query.collect()Key Benefits
Incremental Processing
- Only process new or modified documents
- Automatically update embeddings and indexes
- Typical 70%+ reduction in compute costs
Complete Lineage Tracking
# Trace any result back to source documents
result = chunks.select(
chunks.text,
chunks.document.fileurl,
chunks.metadata
).where(chunks.embeddings.similarity(query_embedding) > 0.8)Experimentation Support
- Try different chunking strategies
- Compare embedding models
- Track all changes automatically
Production Ready
- Same code works in development and production
- Built-in versioning and rollback
- Efficient resource utilization
See It In Action
- Interactive Demo: Multi-LLM Benchmark
- Tutorial: Document Indexing and RAG
- Example: Incremental Prompt Engineering
Getting Started
- Check out our 10-minute tutorial
- Join our Discord community
