Skip to content

DOCUMIND.md

Latest commit

 

History

History
94 lines (69 loc) · 3.04 KB

DOCUMIND.md

File metadata and controls

94 lines (69 loc) · 3.04 KB

DocuMind AI — Case Study

🎯 Problem

Organizations and individuals with large document collections face a critical challenge: finding specific information quickly. Traditional keyword search fails because:

  • Documents contain domain-specific terminology
  • Information is scattered across multiple files
  • Context is lost in simple text matching
  • No way to ask natural language questions

The real cost: Hours wasted searching through PDFs instead of getting work done.

💡 Solution

Built DocuMind AI — an intelligent document Q&A system powered by Retrieval-Augmented Generation (RAG).

Architecture Decisions

Component Choice Why
Vector DB Pinecone Managed, scalable, sub-50ms queries
LLM GPT-4o-mini Best cost/performance ratio for Q&A
Framework LangChain Mature RAG tooling, document loaders
API FastAPI Async, auto-docs, Python ecosystem

How It Works

flowchart TD
    %% Ingestion Flow
    subgraph Ingestion
        direction LR
        DOC[📄 Document] --> CHUNK[Chunk]
        CHUNK --> EMB1[Embed]
        EMB1 --> STORE[(Pinecone Store)]
    end

    %% Query Flow
    subgraph Query
        direction LR
        Q[❓ Question] --> EMB2[Embed]
        EMB2 --> SEARCH[Search]
    end

    %% Execution
    STORE -->|Context| SEARCH
    SEARCH --> LLM((LLM))
    LLM --> ANS[Answer + Citations]
    
    classDef data fill:#1e293b,stroke:#475569,stroke-width:1px,color:#e2e8f0;
    classDef process fill:#047857,stroke:#34d399,stroke-width:2px,color:#fff;
    classDef db fill:#b45309,stroke:#fbbf24,stroke-width:2px,color:#fff;
    classDef ai fill:#4c1d95,stroke:#a78bfa,stroke-width:2px,color:#fff;

    class DOC,Q,ANS data;
    class CHUNK,EMB1,EMB2,SEARCH process;
    class STORE db;
    class LLM ai;
Loading

Key Technical Decisions

  1. Semantic Chunking: Split documents by meaning, not just character count
  2. Overlap Strategy: 200-char overlap prevents losing context at chunk boundaries
  3. Citation System: Every answer includes source chunks for verification
  4. Streaming: Responses stream token-by-token for perceived speed

📊 Results

Metric Value
Answer accuracy 95%+ with citations
Response time < 3 seconds average
File formats PDF, DOCX, TXT
Deployment Vercel serverless

Lessons Learned

  • Chunk size matters: 1000 chars with 200 overlap was optimal
  • Prompt engineering: Clear system prompts dramatically improve answer quality
  • Cost management: GPT-4o-mini is 10x cheaper than GPT-4 with similar quality for Q&A

🔗 Links