app icon
Document Review Agent
0.0.2

LocalDocument Review Agent: Supports the review of various types of documents, including tender documents, official documents, contracts, and materials, with support for non-compliant document detection. 【 View Details and Download Template File】

sawyer-shi/doc-review-agent959 installs

Document Review Agent

A powerful Dify plugin providing comprehensive AI-powered document review capabilities for various types of documents including tender documents, official documents, contracts, and materials, with support for non-compliant document detection. Supports intelligent document parsing, rule-based auditing, risk aggregation, and annotated document generation with professional-grade quality and flexible configuration options.

Version Information

  • Current Version: v0.0.2
  • Release Date: 2026-04-13
  • Compatibility: Dify Plugin Framework
  • Python Version: 3.12

Version History

  • v0.0.2 (2026-04-13):
    • Added integrated slice audit tool (parse -> load rules -> audit -> aggregate -> annotate -> revise)
    • Added integrated simple/full-text audit tool for short document single-loop auditing
    • Added template slice audit tool with required and optional
    • Added template full-text audit tool with required and optional
    • Added template comparators: and
    • Added template risk code normalization to style and aligned output fields for aggregation/annotation
    • Improved no-risk handling in (returns original reviewed file with instead of failing)
    • Reorganized provider tool exposure and YAML definitions around integrated top-level tools
  • v0.0.1 (2026-04-05): Initial release with local document review capabilities

Quick Start

  1. Install plugin in your Dify environment

  2. Download Rules Template and Sample Files:
    https://github.com/sawyer-shi/awsome-dify-agents/blob/master/src/doc-review-agent/agent_test_files/review_rules_research_en.csv

  3. Configure your LLM model settings. Also note: To prevent timeout, you can modify the parameter PLUGIN_MAX_EXECUTION_TIMEOUT to increase processing time!!!

  4. Upload your document and start the review process. Results are as follows:

Key Features

  • Four Integrated Audit Tools: Slice/non-template, full-text/non-template, slice/template, and full-text/template workflows

  • Template Baseline Review: Template-based findings use normalized risk codes like for consistent downstream tagging

  • Hybrid Rule + Template Aggregation: Optional can run together with template audit and merge into one unified risk payload

  • Structured Risk Pipeline: Audit -> aggregation -> annotation -> revision with consistent data schema across workflows

  • High-Quality Output Files: Reviewed (annotated) and revised outputs with configurable JSON/file output modes

  • Flexible Control Knobs: Slice strategy, audit strategy, merge policy, merge strategy, language, and output settings

  • No-Risk Safe Handling: When no risks are found, the workflow returns a valid reviewed file instead of failing

  • Multi-Language Reasoning: Supports zh/en/ja/ko/es/fr/de/pt/ru/ar outputs

Core Features

1) Doc Slice Audit ()

Integrated non-template slice auditing for larger documents.

  • Required: , ,
  • What it does (6 steps):
    1. Document slicing
    2. Rule loading
    3. Chunk auditing
    4. Risk aggregation
    5. Document annotation
    6. File revision
  • Best for: contracts/tenders where chunk-level analysis is preferred

2) Doc Audit ()

Integrated non-template full-text auditing for short documents.

  • Required: , ,
  • What it does (6 steps):
    1. Load review document
    2. Rule loading
    3. Full-text rule audit
    4. Risk aggregation
    5. Document annotation
    6. File revision
  • Best for: shorter documents where whole-text context is important

3) Doc Slice Audit Template ()

Integrated template-based slice auditing.

  • Required: , ,
  • Optional: (runs rule audit + template audit together when provided)
  • What it does (8-step pipeline):
    1. Slice review document
    2. Slice template document
    3. Rule loading (optional input; step kept in progress output)
    4. Rule-based chunk audit (runs when is provided, otherwise marked as skipped)
    5. Template chunk comparison audit
    6. Risk aggregation
    7. Document annotation
    8. File revision
  • Output semantics: template findings use normalized codes (, , ...), severity from LLM ()

4) Doc Audit Template ()

Integrated template-based full-text auditing.

  • Required: , ,
  • Optional: (runs rule audit + template audit together when provided)
  • What it does (8-step pipeline):
    1. Load review document
    2. Load template document
    3. Rule loading (optional input; step kept in progress output)
    4. Rule-based full-text audit (runs when is provided, otherwise marked as skipped)
    5. Full-text template comparison audit
    6. Risk aggregation
    7. Document annotation
    8. File revision
  • Best for: short-form baseline checks against a model template

Shared Output and Controls

  • JSON output: or
  • File output: revised only, or reviewed + revised
  • Revision behavior: choose merge strategy and whether to apply revisions back to source text
  • No-risk behavior: returns a valid reviewed file with

Technical Advantages

  • LLM-Powered Analysis: Leverages advanced LLM models for intelligent document understanding
  • Rule-Based Auditing: Flexible rule system for customizable review criteria
  • Chunk-Based Processing: Efficient handling of large documents through intelligent slicing
  • Risk Deduplication: Smart aggregation to eliminate redundant findings
  • Annotated Output: Professional document output with clear risk indicators
  • Multi-Format Support: Optimized for docx format with extensibility for other formats
  • Configurable Audit Levels: Support for strict and lenient auditing modes
  • Real-Time Processing: Efficient workflow for timely document review

Requirements

  • Python 3.12
  • Dify Platform access
  • Configured LLM model
  • Required Python packages (installed via requirements.txt):
    • dify_plugin>=0.5.0
    • python-docx>=1.1.2
    • openpyxl>=3.1.5

Installation & Configuration

  1. Install required dependencies:

  2. Configure your LLM model in plugin settings

  3. Install plugin in your Dify environment

Usage

Choose the Right Tool

A) Non-template Slice Audit

Use when you have a rule file and need chunk-level review.

  • Required: , ,
  • Recommended options: , , ,

B) Non-template Full-Text Audit

Use when you have a rule file and the document is short enough for full-text auditing.

  • Required: , ,
  • Recommended options: , ,

C) Template Slice Audit

Use when template compliance is required at chunk level.

  • Required: , ,
  • Optional: for hybrid rule + template audit
  • Notes: template findings are normalized to style risk codes

D) Template Full-Text Audit

Use when template compliance is required for the full document.

  • Required: , ,
  • Optional: for hybrid rule + template audit

Typical Output

  • A JSON summary (or detailed JSON if enabled)
  • A reviewed (annotations)
  • A revised (merged or applied revisions)

Supported Document Formats

  • Input: .docx (Microsoft Word)
  • Output: .docx (Microsoft Word with annotations)

Notes

  • Document parsing is optimized for docx format
  • Chunk size can be adjusted based on document complexity
  • Audit level affects the strictness of rule application
  • Risk aggregation uses intelligent deduplication to avoid redundant findings
  • Annotation style currently supports comment-based annotations
  • Large documents are processed efficiently through chunking
  • All tools require a configured LLM model for operation

Developer Information

  • Author:
  • Email: [email protected]
  • License: Apache License 2.0
  • Source Code:
  • Support: Through Dify platform and GitHub Issues

License Notice

This project is licensed under Apache License 2.0. See LICENSE [blocked] file for full license text.


Ready to review your documents with AI-powered intelligence?

CATEGORY
Tool
TAGS
PRODUCTIVITYUTILITIES
VERSION
0.0.2
sawyer-shi·04/14/2026 08:07 AM
REQUIREMENTS
LLM invocation
Tool invocation
App invocation
Endpoint registration
Maximum memory
256MB
Maximum storage
1MB