docgaurd 0.1.11

High-performance document intelligence gateway and safety guardrail engine.

DocGaurd (Document Intelligence Gateway)

DocGaurd (Document Intelligence Gateway) is a high-performance document validation, security scanning, quality guardrail, and exact token counting engine. Built in Rust with native Python bindings via PyO3, DocGaurd sits between raw document ingestion and downstream LLM/RAG pipelines to prevent system exploitation, database bloat, and unexpected API costs.

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Features • Installation • Quick Start • Python API • Telemetry Schema • Supported Formats • Examples • License

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Features

• ✨ Real GPT Tokenization - Integrates high-performance tiktoken-rs in Rust to calculate exact GPT token budgets (not approximations) for models like GPT-4, GPT-3.5, Claude, or LLaMA.
• ⚡ Multi-Format Support - Seamlessly extracts text and parses metadata from PDF, TXT, MD, DOCX, PPTX, XLSX, CSV, JSON, XML, and HTML files.
• 🛡️ Ingestion Security - Built-in security scanners inspect compressed documents and file headers to intercept Zip bombs, compression bombs, and oversized resource limits before they reach system memory.
• 🔍 Text Quality & OCR Necessity Detection - Evaluates page text density, whitespace-to-character ratio, and empty page signals to flag scanned/image-only documents (requires_ocr) before vector database embedding.
• 🚀 Native Parallel Batch Processing - Utilizes Rust's concurrent work-stealing thread pool (Rayon) to process thousands of files or directory trees in parallel with zero GIL serialization.
• 💾 Global De-duplication - Computes high-performance SHA-256 content hashes in parallel to identify and skip exact duplicate files inside a batch queue automatically.
• 💰 Dynamic Cost Estimation - Estimates LLM input cost and vector database embedding cost dynamically before making external API requests.
• 🎯 Intelligent Agent Routing - Classifies text based on heuristic token frequencies and assigns a target downstream AI Agent (e.g., LegalAgent, ProcurementAgent).

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Installation

From PyPI (Recommended)

Install pre-compiled native binary wheels instantly on Windows, Linux, or macOS:

pip install docgaurd

(No Rust compilers, C-libraries, or compilation tools are required on the host system).

From Source

git clone https://github.com/JIVTESH28/docgaurd.git
cd docgaurd
pip install .

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Quick Start

Initialize the Analyzer

import json
import docgaurd

# Initialize the gateway analyzer with custom thresholds
analyzer = docgaurd.DocumentAnalyzer({
    "target_model": "gpt-4",                   # Target context window check
    "tokenizer_name": "cl100k_base",           # Tiktoken profile
    "embedding_rate_per_million": 0.02,        # Cost per 1M tokens ($)
    "llm_input_rate_per_million": 5.00,        # Cost per 1M tokens ($)
    "max_file_size": 52428800                  # Max file size (50MB)
})

Python API Usage

Single File Ingestion (Local Disk)

report_str = analyzer.analyze_file("contract.pdf")
report = json.loads(report_str)
print(f"Tokens: {report['token_count']} | RAG Ready: {report['rag_ready']}")

In-Memory Bytes Ingestion (API Uploads)

uploaded_bytes = b"Sample document text buffer."
report_str = analyzer.analyze_bytes(uploaded_bytes, "invoice.txt")
report = json.loads(report_str)
print(f"Domain Class: {report['document_class']} | RAG Ready: {report['rag_ready']}")

Natively Parallel Batch Processing

file_list = ["agreement.docx", "data.xlsx", "spec.pdf"]
batch_report_str = analyzer.analyze_batch(file_list)
batch_report = json.loads(batch_report_str)

print(f"Successful files: {batch_report['summary']['successful_files']}")
print(f"Duplicates skipped: {batch_report['summary']['duplicate_files']}")

Directory Ingestion (Recursive Scan)

dir_report_str = analyzer.analyze_directory("./archive", recursive=True)
dir_report = json.loads(dir_report_str)
print(f"Total directory tokens: {dir_report['summary']['total_tokens']}")

Ultra-Fast Single-Metric Bypasses

If you only need a single metric and want to bypass the rest of the gateway analysis pipeline (such as security checks, cost estimation, and domain classification), use the sub-millisecond helpers:

# Raw metric count helpers (File-based)
word_count = analyzer.count_words("document.docx")
char_count = analyzer.count_chars("document.docx")
token_count = analyzer.count_tokens("document.docx")

# Raw metric count helpers (Byte-based)
token_count = analyzer.count_tokens_bytes(uploaded_bytes, "invoice.txt")

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Telemetry Output Schema

DocGaurd generates a comprehensive, metadata-rich telemetry report for every analyzed file:

{
  "file_name": "contract_agreement.pdf",
  "file_type": "pdf",
  "sha256": "07c270b274dae324f906e0aa3a8d606471931e9c1afc241ddbc8f9ae52baffe7",
  "token_count": 2424,
  "word_count": 1612,
  "character_count": 11448,
  "page_count": 4,
  "requires_ocr": false,
  "quality_score": 0.8,
  "duplicate": false,
  "security_risk": "low",
  "fits_context": true,
  "rag_ready": true,
  "requires_summarization": false,
  "recommended_chunking": "semantic chunking",
  "document_class": "Legal",
  "recommended_agent": "LegalAgent",
  "estimated_embedding_cost": 0.0,
  "estimated_llm_cost": 0.0121,
  "processing_time_ms": 12.34
}

Telemetry Field Descriptions

Field	Type	Description
file_name	String	Base name of the analyzed file.
file_type	String	Lowercase file extension (e.g. pdf, docx, txt).
sha256	String	Cryptographic SHA-256 hash representing the exact content payload.
token_count	Integer	Exact token count matching the selected model tokenizer profile.
word_count	Integer	Number of words counted based on unicode whitespace dividers.
character_count	Integer	UTF-8 character length of the extracted document text.
page_count	Integer	Page count (e.g. PDF pages, PowerPoint slides, Excel sheets, estimated text lines).
requires_ocr	Boolean	Flags true if document has page structures but low text density (image-only scanned).
quality_score	Float	Cleanliness index (0.0 - 1.0) graded by density, metadata, ratio, and OCR markers.
duplicate	Boolean	Flags true if identical SHA-256 has already been processed in the concurrent batch queue.
security_risk	String	Security score (low, medium, high) validating Zip bombs and size thresholds.
fits_context	Boolean	Checks if token_count fits inside the target model's context window.
rag_ready	Boolean	Evaluates suitability for search databases (true if secure, non-scanned, and clean).
requires_summarization	Boolean	Recommends pre-summarizing if the token count or page density is excessively large.
recommended_chunking	String	Suggested chunking strategy (no chunking, fixed, semantic, hierarchical, agentic).
document_class	String	Classified topical domain (Finance, Procurement, Legal, HR, Tech Doc, Research, etc.).
recommended_agent	String	Recommended target downstream AI Agent target (e.g. LegalAgent).
estimated_embedding_cost	Float	Predicted vector database indexing cost.
estimated_llm_cost	Float	Predicted input processing cost.
processing_time_ms	Float	Internal Gateway execution latency in milliseconds.

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Supported Formats

Format	Extension	Extraction Method	Key Features
PDF	.pdf	Native lopdf Parser	Structural reading, scanned detection, page extraction
Word	.docx	Native docx XML Parser	Direct paragraph and table text extraction
PowerPoint	.pptx	Native pptx XML Parser	Shape text, slide processing, bullet analysis
Excel	.xlsx	Calamine Engine	Spreadsheet parsing, cell extraction, rows estimation
CSV	.csv	CSV Parser	Direct row, column parsing, delimiter validation
Plain Text	.txt, .md	Unicode Parser	Streaming flat extraction, lossy fallback encoding
JSON	.json	Serde JSON	Recursive nested key-value string extraction
XML	.xml	Quick XML Parser	Tag-stripped text, element-wise traversal
HTML	.html	Quick XML Parser	Element parsing, script/style extraction filtering

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Configuration Limits

Setting	Default Value	Purpose
target_model	"gpt-4"	Target context size limit check
tokenizer_name	"cl100k_base"	Tokenizer profile (cl100k_base, r50k_base, p50k_base)
max_file_size	52,428,800 bytes (50MB)	Intercept oversized documents
embedding_rate_per_million	$0.02	Custom embedding cost rate
llm_input_rate_per_million	$5.00	Custom LLM input rate

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Examples

Example 1: RAG Ingestion Security & Quality Gatekeeper

Ensure that only secure, high-quality, digital documents enter your vector database:

import json
import docgaurd

analyzer = docgaurd.DocumentAnalyzer()
report = json.loads(analyzer.analyze_file("user_upload.pdf"))

# Intercept risks at the gateway
if report["security_risk"] == "high":
    raise ValueError(f"CRITICAL: Security exception triggered for {report['file_name']}")

if report["requires_ocr"]:
    print(f"Routing {report['file_name']} to hardware-accelerated OCR pipeline.")
elif not report["rag_ready"]:
    print(f"Skipping {report['file_name']} due to low text quality score: {report['quality_score']}")
else:
    print(f"Ingesting clean document text. Context Size: {report['token_count']} tokens.")

Example 2: API Cost Budgeting & Model Window Check

Calculate API transaction costs and verify if a document fits within a model's context window:

import json
import docgaurd

analyzer = docgaurd.DocumentAnalyzer({
    "target_model": "gpt-3.5-turbo",
    "llm_input_rate_per_million": 1.50
})

report = json.loads(analyzer.analyze_file("long_transcript.txt"))

if not report["fits_context"]:
    print(f"Document exceeds target context window. Recommended chunking strategy: {report['recommended_chunking']}")
else:
    print(f"Document fits. Estimated processing cost: ${report['estimated_llm_cost']:.4f}")

Example 3: Hardware-Accelerated OCR Integration (Metal/CUDA)

Incorporate unified OCR for scanned files using the docgaurd_ocr.py module:

import json
from docgaurd_ocr import OcrDocumentAnalyzer

# Initialize unified OcrDocumentAnalyzer (auto-routes to Apple Metal MPS or CUDA)
gateway = OcrDocumentAnalyzer()

report_json = gateway.analyze_file("scanned_receipt.jpg")
report = json.loads(report_json)

print(f"OCR Text: {report['text']}")
print(f"OCR Tokens: {report['token_count']} | RAG Ready: {report['rag_ready']}")

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License

This project is licensed under the MIT License.