githubinferredactive
LLM-analytical-agent
provenance:github:HIIAYUSHI/LLM-analytical-agent
WHAT THIS AGENT DOES
The LLM-analytical-agent is a Python-based tool designed for SQL reasoning, statistical analysis, and identifying potential hallucinations in large language models. It operates on the GitHub platform and aims to provide a self-correcting analytical capability. This agent is particularly useful for developers and data scientists working with LLMs who need to ensure the accuracy and reliability of their models' outputs. Its ability to detect hallucinations makes it a valuable asset for building robust and trustworthy AI applications.
PROBLEM IT SOLVES
This agent solves the problem of ensuring the accuracy and reliability of SQL reasoning and statistical analysis performed by large language models. It allows users to automate the detection of hallucinations, saving time and effort compared to manual verification.
CAPABILITIES & CONSTRAINTS
TECH & STACK
pythonlarge-language-modelsllm-agentstatistical-analysishallucination-detectionrag
README
\# Self-Correcting LLM Analytical Agent A \*\*Self-Correcting Large Language Model (LLM) Agent\*\* designed for \*\*SQL reasoning, statistical analysis, and hallucination detection\*\*. The system evaluates LLM-generated outputs using analytical validation and statistical grounding to improve reliability in data analysis tasks. --- \## Overview Large Language Models often generate \*\*hallucinated results\*\* when performing analytical reasoning or SQL-based data analysis. This project introduces a \*\*self-correcting analytical pipeline\*\* that: \* Generates analytical responses using an LLM \* Executes SQL queries on structured databases \* Performs statistical validation \* Detects hallucinations using evaluation metrics \* Corrects outputs through analytical feedback loops The system is designed as a \*\*research-oriented framework\*\* for improving \*\*trustworthy AI in data analysis workflows\*\*. --- \## Key Features \* LLM-based analytical reasoning \* SQL query generation and execution \* Retrieval-Augmented Generation (RAG) \* Statistical hypothesis testing \* Automated hallucination detection \* Evaluation metrics for analytical correctness \* Modular research pipeline \* Interactive interface using Streamlit --- \## Project Architecture User Query ↓ LLM Reasoning Agent ↓ SQL Generation ↓ Database Execution ↓ Statistical Analysis Engine ↓ Evaluation Metrics ↓ Hallucination Detection ↓ Corrected Analytical Output --- \## Tech Stack \* Python \* Streamlit \* Pandas \* NumPy \* Scikit-learn \* SQL \* Retrieval-Augmented Generation (RAG) --- \## Project Structure ``` llm-analytical-agent agent.py app.py rag.py prompting.py research\_pipeline.py stats\_engine.py hypothesis\_engine.py evaluation\_metrics.py config.py run\_app.py requirements.txt README.md data/ chinook.db sakila.db sql-murder-mystery.db ``` --- \## Example Use Case Example analytical question: \*\*"Is there a statistically significant difference in sales across music genres?"\*\* Pipeline execution: 1\. LLM generates SQL query 2\. Database executes query 3\. Statistical engine performs hypothesis testing 4\. Evaluation module checks numerical grounding 5\. System flags potential hallucinations Output includes: \* Statistical results \* Analytical explanation \* Confidence evaluation --- \## Evaluation Metrics The system evaluates analytical reliability using metrics such as: \* \*\*Hallucination Rate (HR)\*\* \* \*\*Numerical Grounding Score (NGS)\*\* \* \*\*Analytical Consistency Score (ACS)\*\* \* \*\*Confidence Calibration Error (CCE)\*\* These metrics help quantify \*\*trustworthiness of LLM-generated analytical outputs\*\*. --- \## Installation Clone the repository: ``` git clone https://github.com/HIIAYUSHI/llm-analytical-agent.git ``` Navigate to the project directory: ``` cd llm-analytical-agent ``` Install dependencies: ``` pip install -r requirements.txt ``` Run the application: ``` streamlit run app.py ``` --- \## Future Improvements \* Integration with advanced LLMs \* Enhanced hallucination detection mechanisms \* Model interpretability modules \* Cloud deployment \* Interactive analytics dashboard --- \## Author \*\*Ayushi Bisht\*\* Student – Data Science \& Statistics Interested in \*\*Machine Learning, AI Systems, and Trustworthy LLMs\*\*
PUBLIC HISTORY
First discoveredMar 21, 2026
IDENTITY
inferred
Identity inferred from code signals. No PROVENANCE.yml found.
Is this yours? Claim it →METADATA
platformgithub
first seenMar 6, 2026
last updatedMar 6, 2026
last crawled2 months ago
version—
README BADGE
Add to your README:
