Zero-SQL Database Assistant: Private Text-to-Pandas Ingestion Pipeline

A production-grade, local-first analytical platform designed to empower operational business groups to run unstructured data audits completely in natural English without database language skill prerequisites. The architecture solves the security and stability risks of dynamic LLM systems by combining structured Pydantic schema validation with an isolated execution sandbox.

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🏗️ Architectural Overview & Security Blueprint

graph TD
    UserCSV[Messy User CSV File] --> DataParser[Pandas Auto Schema & Values Extractor]
    DataParser --> IngestionPrompt[Contextual Schema Prompt Assembly]
    UserPrompt[User Question: 'Find Q3 top profits...'] --> IngestionPrompt
    
    IngestionPrompt --> LocalLLM[(Local Ollama Llama 3.1 8B)]
    LocalLLM --> StructOutput[Instructor + Pydantic Serialization]
    
    StructOutput --> RawCode[Deterministic Code String Output]
    RawCode --> Guardrail[Pre-Execution String Keyword Filter]
    Guardrail --> Sandbox[Restricted Scoped eval Environment]
    Sandbox --> Render[Streamlit Interactive Matrix Dashboard]

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Advanced Engineering Strategy Highlights

1. Deterministic Execution Contracts: Bypasses conversational noise (chat text) completely by wrapping the local Llama connection layer with instructor. The system explicitly constrains outputs into deterministic JSON parameters (thought_process, pandas_code).
2. Dynamic Schema Bridging: Avoids parsing breakdowns on raw corporate datasets containing chaotic column headers (e.g., "Total Profit ($)", "product_ID ") by mapping the column names into the system prompt context at runtime.
3. Decoupled Namespace Sandboxing: Eliminates host command execution risks (prompt injection payload hacks) by routing text through a restricted scope environment that blocks global primitives (__builtins__) and filters harmful code sequences like .drop() or import os.

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⚙️ Installation & Lifecycle Guide

1. Engine Prerequisites

Ensure your background local Ollama engine server is active:

ollama run llama3.1

2. Sandbox Setup

Initialize a pristine local virtual environment and install the verified packages:

python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt

3. Launching the App Workspace

Start the system using the native web application dashboard port:

streamlit run app.py

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📊 Verification Metrics

Interaction Vector Verified	Vulnerability Handled	System Action Verdict
Standard User Request	Schema Header Matching	Success (Code compiled using messy inputs)
Malicious Injection Call	Command Intrusion (import os)	Terminated (Caught by sandbox guardrail)
Destructive Data Call	Matrix Frame Drops (df.drop)	Blocked (Flagged as non-readable query pattern)