Vault-Lens 0.1.3
Vault_Lens lets you run private datasets locally and gives Ollama a lens to see in the vault. Raw data is never touched by the llm.
License: MIT
Requires Python: >=3.7

# Privacy-First Automated Data Observability Pipeline

## The Business Value
In modern data environments, companies face two major hurdles: **Data Privacy (GDPR/HIPAA)** and **Data Integrity**.

Most "AI Data Assistants" require uploading sensitive raw data to a third-party cloud, risking privacy breaches. Furthermore, LLMs often "hallucinate" mathematical statistics.

## Validation
Tested against the same 10K insurance dataset used in my [Insurance Claims Prediction](https://github.com/CharSiu8/insurance-claims-prediction-logistic-regression) project. The auditor correctly identified the same 982 null values in `credit_score` and 957 in `annual_mileage` that I found through manual EDA—confirming the pipeline replicates expert-level data quality checks automatically.

**This project solves these issues by:**
1. **Local-First Auditing:** All statistical analysis (null detection, type inconsistency checks, date validation) happens locally using Pandas—raw data never leaves your machine. 
2. **Hybrid Intelligence:** Deterministic Python logic ensures 100% mathematical accuracy. AI is used only to *interpret* the pre-computed findings and suggest remediation plans.
3. **Automation:** Built as a modular pipeline that can be integrated into automated workflows, rather than a manual "chat" interface.
4. **Flexible AI Providers:** Choose between OpenAI (cloud) or Ollama (fully local)—enabling 100% offline operation for maximum privacy.

## Installation
```bash
git clone https://github.com/CharSiu8/data_auditor.git
cd data_auditor
pip install -r requirements.txt
```

## Quick Start

### Option A: Fully Local (No API Key Needed)
1. Install [Ollama](https://ollama.ai)
2. Pull a model: `ollama pull llama3.2`
3. Run: `python main.py --model ollama`

### Option B: Cloud (OpenAI)
1. Create `.env` file: `OPENAI_API_KEY=your-key-here`
2. Run: `python main.py --model openai`

### Command Line Options
```bash
python main.py --model ollama --file your_data.csv
```
| Argument | Options | Default | Description |
|----------|---------|---------|-------------|
| `--model` | `openai`, `ollama` | `openai` | AI provider to use |
| `--file` | any CSV path | `test_data.csv` | File to audit |

## Tech Stack & Architecture
- **Language:** Python 3.x
- **Data Engineering:** Pandas
- **AI Integration:** OpenAI API (GPT-4o-mini) or Ollama (Llama 3.2, local)
- **Security:** python-dotenv (Environment Variable Management)
- **Version Control:** Git/GitHub

### Project Structure
| File | Purpose |
|------|---------|
| `main.py` | Application entry point and pipeline coordinator |
| `auditor.py` | Statistical engine—performs all data quality checks locally |
| `reporter.py` | Serializes audit results to JSON |
| `analyzer.py` | Routes to OpenAI or Ollama for AI interpretation |
| `feedback.py` | Sends user feedback to developer via Discord webhook |
| `.env` | Secure storage for API keys (ignored by Git) |

### Data Flow
```
CSV File → auditor.py (local analysis) → reporter.py (JSON) → analyzer.py (AI interpretation) → Summary
```

## Privacy Architecture
- **What stays local:** Raw data, all statistical computations
- **What is sent to AI:** Only audit metadata (column names, data types, row indices with issues)—no actual data values are transmitted
- **With Ollama:** Everything stays local—zero data leaves your machine


## Impact
By automating the Exploratory Data Analysis (EDA) phase, this tool reduces the "Data Cleaning" bottleneck—which typically takes up 80% of a Data Scientist's time—allowing for faster, safer insights.
## Feedback
Built-in feedback system lets users send comments directly to the developer. Help improve VaultLens by sharing your experience after running an audit.