Overview

Data Chat Interface is an AI-powered analytics platform that enables non-technical users to query and analyze datasets using natural language. Upload a CSV or Excel file, ask questions in plain English, and receive insights, visualizations, and data summaries.

The system combines large language models with traditional data analysis techniques to provide accurate, context-aware responses to user queries.

Problem Statement

Business users often need to analyze data but face several barriers:

  • Lack of SQL or programming knowledge required for data analysis
  • Dependency on data analysts for simple queries and reports
  • Time delays in getting answers to urgent business questions
  • Difficulty understanding complex data structures and relationships
  • Need for self-service analytics that doesn't require technical training

The solution democratizes data access by allowing anyone to interact with datasets using conversational language.

Technical Approach

A hybrid architecture combining AI and traditional analytics:

Backend Architecture

  • FastAPI: High-performance Python API for file processing and query handling
  • OpenAI GPT-4: Natural language understanding and query generation
  • Pandas: Data manipulation and analysis engine
  • ChromaDB: Vector database for semantic search and context retrieval

Frontend

  • Blazor WebAssembly: Interactive chat interface with real-time responses
  • Chart.js: Dynamic visualization generation based on query results
  • SignalR: Real-time communication between client and server

AI Pipeline

  • Schema Analysis: Automatic detection of data types, relationships, and patterns
  • Query Translation: Converting natural language to Pandas operations
  • Result Interpretation: Generating human-readable explanations of findings
  • Context Management: Maintaining conversation history for follow-up questions

Key Features

Natural Language Queries

Ask questions like "What was the average sales by region last quarter?" and get instant answers

Automatic Visualizations

System suggests and generates appropriate charts based on the query type and data

Multi-File Support

Upload and query across multiple related datasets with automatic relationship detection

Conversation History

Build on previous queries with follow-up questions that maintain context

Export Results

Download query results, charts, and insights in multiple formats

Current Status

The project is currently in active development with the following components complete:

  • Core NLP-to-Pandas translation engine (90% complete)
  • File upload and schema detection (100% complete)
  • Basic chat interface (80% complete)
  • Visualization engine (70% complete)

Expected public beta launch: Q1 2026