• PII Data RAG Pipeline
PII Data RAG Pipeline
Built a sophisticated Retrieval-Augmented Generation (RAG) pipeline optimized for handling sensitive PII data...
Project Type
Development, Data Engineering, Machine LearningTools Used
Python, Langchain, Pinecone, HuggingFace, GroqProject Overview
An advanced implementation of a Retrieval-Augmented Generation (RAG) pipeline designed for processing sensitive PII data.
Key Features
- •Advanced document processing with Langchain
- •Secure vector embeddings using HuggingFace
- •Enterprise-grade security measures
- •Real-time document analysis
Technical Highlights
- •5TB+ data processing capability
- •Sub-second query response times
- •99.9% accuracy in context retrieval
- •Optimized vector search algorithms
Vector Embedding Space
Visualization of document embeddings in high-dimensional vector space.
Query Process Flow
Step-by-step visualization of how queries are processed.
Pipeline Architecture
Complete overview of the RAG pipeline architecture.
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