🤖 AI-Powered Document Intelligence

RAG Chatbot - Chat with PDF

Transform your PDFs into intelligent conversational assistants. Ask questions, extract insights, and get instant answers from your documents using advanced Retrieval Augmented Generation (RAG) technology.

99.9%
Accuracy Rate
<2s
Response Time
24/7
Availability
RAG Chatbot Illustration

Powerful Features

Everything you need for intelligent document interaction

Advanced AI Understanding

Powered by GPT-4 Turbo for deep comprehension and accurate responses

Persistent Memory

Vector database stores all document knowledge for instant retrieval

Real-time Responses

Sub-second query processing with streaming responses

Enterprise Security

End-to-end encryption, SOC 2 compliant infrastructure

Multi-format Support

PDFs, Word docs, presentations, and more

24/7 Availability

Always-on chatbot accessible from anywhere

N8N Workflow Architecture

Complete RAG pipeline from document upload to intelligent responses

N8N RAG Workflow Diagram

Document Processing Pipeline

  • 1.PDF upload via webhook trigger
  • 2.Text extraction and OCR processing
  • 3.Intelligent chunking with overlap
  • 4.Vector embedding generation (OpenAI)
  • 5.Storage in Pinecone vector database

Query Processing Pipeline

  • 1.User query received via chat interface
  • 2.Query embedding generation
  • 3.Semantic similarity search in vector DB
  • 4.Context retrieval and re-ranking
  • 5.GPT-4 answer generation with citations

How It Works

Discover the technology behind intelligent document conversations

1

Document Upload & Processing

Intelligent PDF parsing and text extraction

Support for multiple PDF formats (scanned, text-based, mixed)
Automatic OCR for scanned documents
Preserves document structure and formatting
Handles large documents (up to 1000 pages)
Batch processing for multiple PDFs
2

Intelligent Text Chunking

Smart segmentation for optimal retrieval

Context-aware chunking (maintains semantic meaning)
Configurable chunk size (default: 1000 tokens)
Overlap strategy to prevent information loss
Respects paragraph and section boundaries
3

Vector Embedding & Storage

High-performance semantic search infrastructure

OpenAI text-embedding-3-large model (3072 dimensions)
Pinecone vector database for lightning-fast retrieval
Automatic indexing and metadata tagging
Scalable to millions of document chunks
4

Contextual Query Processing

Advanced RAG pipeline for accurate answers

Semantic similarity search (top-k retrieval)
Re-ranking for relevance optimization
Context window management (up to 128k tokens)
GPT-4 Turbo for answer generation
Source citation with page numbers
Real-World Applications

Transform Your Business Operations

See how businesses leverage RAG chatbots across industries to boost efficiency, reduce costs, and unlock new insights.

Legal Document Analysis

Quickly extract insights from contracts, case files, and legal briefs

Contract review
Case law research
Compliance checking

Research & Academia

Accelerate literature review and research paper analysis

Paper summarization
Citation extraction
Methodology comparison

Customer Support

Instant answers from product manuals and documentation

Troubleshooting guides
FAQ automation
Product knowledge base

Financial Services

Analyze reports, statements, and regulatory documents

Financial report analysis
Risk assessment
Compliance monitoring
Technical Specs

Technical Specifications

AI Models

  • Embedding Model:text-embedding-3-large
  • LLM:GPT-4 Turbo
  • Context Window:128K tokens
  • Embedding Dimensions:3072

Infrastructure

  • Vector Database:Pinecone
  • Orchestration:N8N
  • Max Document Size:100 MB
  • Supported Formats:PDF, DOCX, TXT

Ready to Transform Your Documents?

Join hundreds of businesses using RAG chatbots to unlock insights from their documents. Start your free trial today.

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