Policy Compliance Auditor

A smart AI-powered Compliance Auditor Assistant designed to analyze documents, extract relevant compliance information, and determine whether they meet specified policy standards such as GDPR. This tool leverages embeddings, similarity search, and LLMs to provide detailed compliance assessments based on custom policies and similar documents.

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Features

• Document Chunk Analysis: Reads and analyzes PDF document chunks for compliance-relevant entities.
• Policy Matching: Retrieves and cross-checks document content against relevant policy rules using semantic search.
• Similar Document Reference: Finds and compares similar documents to strengthen audit decisions.
• Compliance Decision: Outputs a clear Compliant or Non-Compliant status with detailed explanations.
• Extensible Tools: Easily integrate additional policy collections and compliance questions.
• Gradio UI: User-friendly interface for uploading documents, entering queries, and viewing compliance reports.
• Dockerized: Ready for containerized deployment.

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Architecture

• Embeddings & Vector Search: Uses a vector database (e.g., ChromaDB) to store and query policy rules and similar documents efficiently.
• Large Language Model (LLM): Applies an LLM to reason about compliance based on the context, retrieved policies, and related documents.
• Gradio Frontend: Provides an interactive web UI for document upload and compliance queries.
• Modular Tools: Custom tools like find_matching_policies and find_similar_documents encapsulate business logic.

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Getting Started

Prerequisites

• Python 3.11+
• Docker (optional, for containerized deployment)
• Access to OpenAI API or other LLM providers like Ollama
• Vector database setup (e.g., ChromaDB)

Installation

1. Clone the repository:

   git clone https://github.com/SilasPenda/Policy-Compliant-Auditing-Agent
   cd policy-compliance-auditor
   

2. Create & activate virtual environment:

   python -m venv .venv
   source .venv/bin/activate (Linux & Mac)
   ./.venv/Scripts/activate (Windows)
   

3. Install requirements:

   python -m pip install --upgrade pip
   pip install -r requirements.txt
   

4. Create .env file and add OPEN_AI_KEY

5. Using NB_1.ipynb, create collections for documents and policies (policy.yaml):

6. Start App

   python app.py