RAG Builder Architecture

Intelligent Retrieval-Augmented Generation system for context-aware chat agents. Built on proven design patterns for automated issue resolution and knowledge retrieval.

RAG System Architecture

PloyD's RAG Builder combines semantic search with grounded LLM responses, ensuring each query is addressed with accurate, context-rich information from your knowledge base.

USER INTERFACE

Chat Interface & Input Processing

Chat Interface
File Upload
Image OCR
Voice Input
Multi-Device
Chat History
PloyD RAG PLATFORM

RAG Processing Engine

Query Processing
Query Analysis
Embeddings
Intent Detection
Knowledge Retrieval
Semantic Search
Reranking
Context Assembly
Response Generation
LLM Processing
Self-Critique
Confidence Score
ABSTRACTED

Infrastructure & Data Sources

Vector Database
Document Store
Code Repository
LLM Services
Data Sync
Monitoring

RAG Workflows

Two core workflows power PloyD's RAG Builder: real-time chat processing and knowledge base updates

Real-time Chat Processing

  1. Query Ingestion: User submits question through chat interface with optional file attachments
  2. Content Processing: Extract text from images (OCR), documents, and voice inputs
  3. Semantic Embedding: Convert query to vector using specialized embedding models
  4. Context Retrieval: Search vector database for most relevant knowledge chunks
  5. Reranking: Refine search results using advanced reranking algorithms
  6. LLM Generation: Generate response using retrieved context with Chain-of-Thought reasoning
  7. Quality Assessment: Self-critique and confidence scoring for response reliability
  8. Response Delivery: Format and deliver response with sources and confidence indicators

Knowledge Base Updates

  1. Source Monitoring: Track changes in documentation, code repositories, and data sources
  2. Data Fetching: Pull latest content from configured knowledge sources
  3. Intelligent Chunking: Process documents and code using AST-based parsing for optimal context
  4. Metadata Enrichment: Add file paths, timestamps, and structural information
  5. Embedding Generation: Convert chunks to vector embeddings using consistent models
  6. Database Upsert: Update vector database with new/modified content
  7. Index Optimization: Maintain search performance and remove outdated content
  8. Validation: Verify knowledge base integrity and search quality

Sample Technology Stack

Here's an example of a tech stack configuration built with enterprise-grade components for reliability, scalability, and performance

Core Processing

  • Orchestration: Python with LangGraph for agentic workflows
  • Embedding Models: NVIDIA NIM nv-embedcode-7b-v1
  • LLM Services: Llama-3.3-70B-Instruct via NVIDIA NIM
  • Reranking: BAAI/bge-reranker-v2-m3

Data & Storage

  • Vector Database: ChromaDB for semantic search
  • Document Processing: LangChain Text Splitters
  • Code Parsing: Abstract Syntax Trees (ASTs)
  • Content Extraction: OCR, PDF, and multimedia processing

Infrastructure

  • Deployment: NVIDIA DGX Cloud
  • Containerization: Docker for portable deployments
  • Monitoring: LangSmith for tracing and evaluation
  • Scaling: Asynchronous task processing

Integration

  • APIs: RESTful APIs for chat and knowledge management
  • Webhooks: Real-time data source synchronization
  • Authentication: Enterprise SSO and access control
  • Multi-modal: Text, image, voice, and document support

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