intermediate2-3 hours
How to Implement RAG in Production: Complete Python Guide
Build production RAG systems with Pixeltable. Handle large datasets, optimize embeddings, and scale reliably.
Challenge
Production RAG requires handling large document collections, managing embeddings, optimizing retrieval, and coordinating multiple systems—adding complexity and operational overhead.
Solution
Pixeltable unifies the entire RAG stack. Automatic document processing, embedding management, vector indexing, and retrieval optimization in one framework.
Implementation Steps
Step 1 of 1Scalable document processing and embedding setup
import pixeltable as pxtfrom pixeltable.functions import openai, huggingface# Create production document storedocuments = pxt.create_table('production_documents', {'document': pxt.Document,'title': pxt.String,'source': pxt.String,'document_type': pxt.String})# Automatic text extraction and chunkingdocuments.add_computed_column(raw_text=pxt.functions.extract_text(documents.document))# Create chunks for retrievalchunks = pxt.create_view('document_chunks', documents)
💡 Automatic text extraction and chunking for production use.
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Key Benefits
70% faster RAG development
Automatic embedding management
Built-in caching cuts API costs 60%
Declarative workflows
Real Applications
•Enterprise knowledge bases
•Customer support chatbots
•Research and analysis platforms
Prerequisites
•Understanding of vector embeddings
•Experience with Python and API integration
Technical Needs
•Python 3.8+
•OpenAI API key for LLM generation
•8GB+ RAM for embedding processing
Performance
Development Speed
vs building from scratch
10x faster
Ready to Get Started?
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