Document Analyzer Agent

A production-grade AI agent that ingests documents (PDF, DOCX, images), extracts structured data, generates summaries, and answers questions about the content.

Data Processing NLP Enterprise

Sample Document

Agent Overview
Architecture
API Specification
Deployment & Scaling
Performance Benchmarks

1. Agent Overview

The Document Analyzer Agent is a multi-modal AI system that processes unstructured documents and converts them into structured, queryable knowledge. It handles PDF, DOCX, XLSX, images (OCR), and scanned documents.

Core Capabilities

Capability	Description	Accuracy
Text Extraction	OCR + native text extraction from all formats	99.2%
Entity Recognition	Names, dates, amounts, addresses, IDs	96.5%
Document Classification	Auto-categorize into 50+ document types	94.8%
Summarization	Executive summaries of 1-100 page documents	N/A (qualitative)
Q&A	Natural language questions about document content	92.3%
Table Extraction	Structured tables from PDFs and images	91.7%

2. Architecture

System Components

Ingestion Layer: Accepts files via REST API, S3 upload, email, or webhook. Validates format, size (max 100MB), and security (malware scan).
Pre-Processing Pipeline: Format detection → OCR (Tesseract/Azure Document Intelligence) → Text cleaning → Chunking (512 token windows with 50 token overlap)
Vector Store: Document chunks embedded via text-embedding-3-small, stored in pgvector (PostgreSQL) with metadata filtering
LLM Orchestrator: Claude API with tool-use for structured extraction. Implements RAG with semantic search + reranking.
Output Formatter: Structured JSON, Markdown reports, or conversational responses based on request type

Technology Stack

Component	Technology
Orchestration	LangGraph (stateful agent graphs)
LLM	Claude Sonnet 4.5 (analysis), Haiku 4.5 (classification)
Embeddings	text-embedding-3-small (OpenAI)
Vector DB	pgvector (PostgreSQL extension)
OCR	Tesseract + Azure Document Intelligence
Queue	Celery + Redis
API	Django REST Framework

3. API Specification

Endpoints

Method	Endpoint	Description
POST	/api/v1/documents/upload	Upload and process a document
GET	/api/v1/documents/{id}/status	Check processing status
GET	/api/v1/documents/{id}/summary	Get document summary
POST	/api/v1/documents/{id}/query	Ask a question about the document
GET	/api/v1/documents/{id}/entities	Get extracted entities
GET	/api/v1/documents/{id}/tables	Get extracted tables as JSON
POST	/api/v1/documents/batch	Batch upload (up to 50 files)

Example Request & Response

POST /api/v1/documents/{id}/query

{
  "question": "What is the total contract value?",
  "response_format": "json"
}

Response:

{
  "answer": "The total contract value is $2,450,000 USD",
  "confidence": 0.97,
  "sources": [
    {"page": 3, "paragraph": 2, "text": "Total Contract Value: USD 2,450,000.00"}
  ]
}

4. Deployment & Scaling

Infrastructure Requirements

Component	Specification	Monthly Cost
API Server	2x c5.xlarge (4 vCPU, 8GB RAM)	$250
Worker Nodes	3x c5.2xlarge (8 vCPU, 16GB RAM)	$750
PostgreSQL + pgvector	db.r6g.large (2 vCPU, 16GB RAM, 500GB SSD)	$200
Redis	cache.r6g.large	$100
S3 Storage	1TB document storage	$25
LLM API Costs	~50,000 documents/month	$800
Total		$2,125/month

Scaling Strategy

Horizontal scaling: Add worker nodes via auto-scaling group (CPU threshold: 70%)
Caching: Cache common queries and document summaries in Redis (TTL: 24h)
Batch processing: Off-peak processing for large uploads via Celery beat
Rate limiting: 100 requests/minute per API key, 1,000 documents/day per organization

5. Performance Benchmarks

Metric	Target	Achieved
Document upload to processed	< 30 seconds (10-page PDF)	18 seconds
Query response time	< 3 seconds	1.8 seconds
Entity extraction accuracy	> 95%	96.5%
OCR accuracy (scanned docs)	> 97%	98.1%
Concurrent users	500+	750
Uptime SLA	99.9%	99.95%