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DataMind AI — Product Requirements & Architecture Document

1. Executive Summary

DataMind AI is an enterprise-grade Intelligent Data & Document Assistant. The platform is designed to bridge the gap between complex databases, unstructured documents, and end-users through natural language processing. By leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI Agents, DataMind AI allows businesses to instantly extract actionable insights from tabular data, SQL databases, and PDF documents.

2. Core Features

2.1 Automated Data Analytics Pipeline

  • Seamless Data Ingestion: Upload CSV or Excel files.
  • Automated Data Cleaning: Intelligent handling of missing values, nulls, and type casting using Pandas.
  • Statistical Summaries: Automated generation of descriptive statistics and distribution metrics.
  • Visual Insights: Dynamic charting and graph generation using Matplotlib/Seaborn.

2.2 Text-to-SQL Engine

  • Natural Language Queries: Users can ask questions in plain English (e.g., "What were our top 5 selling products last month?").
  • AI-Generated SQL: OpenAI-powered translation of natural language to complex SQL queries (JOINs, CTEs, Aggregations).
  • Secure Execution: Read-only execution protocols preventing destructive database queries.
  • Contextual Explanations: AI explains the retrieved data in human-readable formats alongside raw results.

2.3 RAG-Powered Document Intelligence

  • Document Parsing: High-accuracy text extraction from PDF and text documents.
  • Semantic Search: Text chunking and embedding generation using OpenAI text-embedding-ada-002.
  • Vector Database: Storage and retrieval utilizing ChromaDB.
  • Source-Cited Answers: Accurate AI responses that explicitly cite the source document and page number to prevent hallucinations.
  • Hybrid Search Capabilities: Combining Vector Search with Keyword Search (BM25) for precision retrieval.

2.4 Autonomous AI Agents (ReAct)

  • LangChain Integration: Advanced chaining and prompt management.
  • Tool Selection: Agents autonomously decide whether to query the database, search a document, or perform a calculation based on the user's prompt.
  • Memory Management: Context-aware conversation buffers for continuous chatting.
  • Structured Outputs: JSON schema validation for predictable frontend rendering.

3. Technology Stack

Layer Technology Purpose
Backend Framework FastAPI (Python) High-performance, asynchronous REST API
Relational Database PostgreSQL & SQLAlchemy Primary data storage and ORM
Vector Database ChromaDB Embedding storage for RAG pipeline
Data Processing Pandas, Numpy Data cleaning, manipulation, and analysis
AI / LLM Core OpenAI API, LangChain Language modeling, Embeddings, AI Agents
Deployment Docker, GitHub Actions Containerization and CI/CD pipelines

4. Product Development Roadmap

Phase 1: Core API & Data Foundation

  • Establish FastAPI backend architecture and PostgreSQL integration.
  • Develop CSV ingestion, Pandas-based automated cleaning, and basic statistical API endpoints.

Phase 2: LLM Integration & Text-to-SQL

  • Integrate OpenAI API with secure prompt engineering.
  • Develop the Text-to-SQL engine with read-only database execution.
  • Implement conversation history for context-aware interactions.

Phase 3: Document Intelligence (RAG)

  • Integrate PDF parsing and semantic text chunking.
  • Implement ChromaDB vector store and embedding generation.
  • Build the RAG pipeline with source citation and hybrid search.

Phase 4: Autonomous Agents

  • Implement LangChain ReAct agents.
  • Define specific tools (SQL Tool, Doc Search Tool, Math Tool).
  • Implement streaming responses and structured JSON outputs.

Phase 5: Production & MLOps

  • Containerize the application using Docker.
  • Setup CI/CD pipelines using GitHub Actions.
  • Implement JWT Authentication, Rate Limiting, and global error handling.
  • Deploy backend and frontend to production servers.