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Triepod.ai Knowledge Base Platform

Enterprise RAG system with vector database integration for semantic document search and AI-powered insights

The system features semantic search capabilities, Hugging Face model integration, and comprehensive document analysis with chunking, embedding generation, and intelligent tagging. Designed for enterprise environments requiring efficient knowledge discovery and AI-powered document insights.

Key Metrics

95%
Search Accuracy
Semantic search precision rate
60%
Token Optimization
Cost reduction through optimization
2 hours
Setup Time
Time to deployment and value
99.9%
System Uptime
Enterprise-grade reliability

Features

Hybrid Vector Database

Combines PostgreSQL for structured data with Pinecone for high-performance vector search and semantic similarity matching.

Advanced Document Processing

Intelligent chunking with heading structure analysis, semantic tag extraction, and multi-language support.

Real-time Search & RAG

Lightning-fast semantic search with retrieval-augmented generation for contextual AI responses.

Hugging Face Integration

Direct API integration with Hugging Face Spaces for text generation, summarization, and image processing.

Enterprise Security

Secure API token management, rate limiting, and comprehensive logging for enterprise compliance.

Intelligent Analytics

Token counting, processing metrics, and detailed analytics for cost optimization and performance insights.

Technology Stack

React
TypeScript
Node.js
Express.js
PostgreSQL
Pinecone
OpenAI
Hugging Face
Supabase
Vite
TailwindCSS