Document Q&A System

Build an intelligent document Q&A system that answers questions from uploaded documents

Learn how to create a powerful document Q&A system that can answer questions based on uploaded documents using Retrieval-Augmented Generation (RAG).

What You'll Build

A sophisticated AI system that:

  • Accepts document uploads (PDF, DOCX, TXT)

  • Processes and chunks documents intelligently

  • Creates searchable embeddings

  • Answers questions with relevant context

  • Provides source citations

Prerequisites

  • BroxiAI account with API access

  • OpenAI API key (for embeddings and chat)

  • Pinecone account (or other vector database)

  • Sample documents for testing

Architecture Overview

Step 1: Set Up Document Processing

Document Upload Component

  1. Add File Upload Component

    • Drag "File Upload" from Input & Output

    • Configure supported formats: PDF, DOCX, TXT, MD

  2. Configure File Settings

    {
  "name": "Document Upload",
  "accepted_formats": [".pdf", ".docx", ".txt", ".md"],
  "max_file_size": "10MB",
  "multiple_files": true
}

Text Extraction

  1. Add Document Loader

    • Find "Document Loader" in Data Processing

    • This extracts text from various file formats

  2. Configure Extraction

    {
  "extract_metadata": true,
  "preserve_formatting": false,
  "chunk_overlap": 200,
  "encoding": "utf-8"
}

Text Chunking

  1. Add Text Splitter

    • Use "Recursive Character Text Splitter"

    • Smart chunking preserves context

  2. Configure Chunking Strategy

    {
  "chunk_size": 1000,
  "chunk_overlap": 200,
  "separators": ["\n\n", "\n", " ", ""],
  "keep_separator": true
}

Step 2: Set Up Vector Database

Pinecone Configuration

  1. Create Pinecone Index

    # Pinecone setup (external)
import pinecone

pinecone.init(
    api_key="your-pinecone-api-key",
    environment="us-west1-gcp-free"
)

# Create index with OpenAI embedding dimensions
pinecone.create_index(
    name="document-qa",
    dimension=1536,  # OpenAI embedding size
    metric="cosine"
)

  2. Add Vector Database Component

    • Drag "Pinecone" from Vector Database section

    • Configure connection settings

  3. Configure Pinecone Component

    {
  "api_key": "${PINECONE_API_KEY}",
  "environment": "us-west1-gcp-free",
  "index_name": "document-qa",
  "namespace": "documents"
}

Embeddings Setup

  1. Add OpenAI Embeddings

    • Find "OpenAI Embeddings" in Embeddings section

    • This converts text to vector representations

  2. Configure Embeddings

    {
  "model": "text-embedding-ada-002",
  "api_key": "${OPENAI_API_KEY}",
  "batch_size": 1000
}

Step 3: Build Document Ingestion Flow

Connect Processing Components

Create this flow for document ingestion:

Component Connections

  1. File Upload → Document Loader

    • Connects uploaded files to text extraction

  2. Document Loader → Text Splitter

    • Sends extracted text for chunking

  3. Text Splitter → OpenAI Embeddings

    • Converts chunks to embeddings

  4. Embeddings → Pinecone

    • Stores vectors in database

  5. Pinecone → Output

    • Confirms successful storage

Step 4: Build Q&A Interface

Question Processing

  1. Add Chat Input

    • For user questions

    • Configure with conversation memory

  2. Add Question Embeddings

    • Another OpenAI Embeddings component

    • Same model as document embeddings

Retrieval System

  1. Add Vector Search

    • Drag "Pinecone Retrieval" component

    • Searches for relevant document chunks

  2. Configure Retrieval

    {
  "top_k": 5,
  "similarity_threshold": 0.7,
  "include_metadata": true,
  "namespace": "documents"
}

Answer Generation

  1. Add OpenAI Chat Model

    • For generating final answers

    • Configure with context-aware prompt

  2. Configure Answer Model

    {
  "model": "gpt-3.5-turbo-16k",
  "temperature": 0.1,
  "max_tokens": 1000,
  "system_prompt": "You are a helpful assistant that answers questions based on provided document context. Always cite your sources and be clear about what information comes from the documents."
}

Step 5: Create the Complete Q&A Flow

Q&A Workflow

Prompt Engineering

Create a sophisticated prompt for the LLM:

You are an expert document analyst. Answer the user's question based on the provided document context.

CONTEXT:
{retrieved_context}

QUESTION: {user_question}

INSTRUCTIONS:
1. Answer based only on the provided context
2. If the context doesn't contain enough information, say so clearly
3. Always cite your sources using [Document: filename, Page: X] format
4. Be precise and factual
5. If asked about something not in the context, explain that the information is not available in the uploaded documents

ANSWER:

Step 6: Add Advanced Features

Source Citation

  1. Metadata Tracking

    {
  "metadata_fields": [
    "source_file",
    "page_number",
    "chunk_index",
    "document_title"
  ]
}

  2. Citation Format

    • Include source file names

    • Add page numbers when available

    • Link to original document sections

Multi-Document Support

  1. Document Organization

    {
  "namespace_strategy": "by_collection",
  "collections": [
    "user_uploads",
    "knowledge_base",
    "policies"
  ]
}

  2. Search Filtering

    • Filter by document type

    • Search within specific collections

    • Date-based filtering

Answer Quality Improvement

  1. Hybrid Search

    • Combine vector similarity with keyword search

    • Use BM25 + vector search

    • Improve retrieval accuracy

  2. Re-ranking

    {
  "reranking": {
    "enabled": true,
    "model": "cross-encoder",
    "top_k_before_rerank": 20,
    "top_k_after_rerank": 5
  }
}

Step 7: Testing and Optimization

Test with Sample Documents

Business Documents

  • Employee handbooks

  • Policy documents

  • Product manuals

  • Technical specifications

Academic Papers

  • Research publications

  • Technical reports

  • Case studies

  • White papers

Test Questions

Examples:
- "What is the company's vacation policy?"
- "How do I set up the authentication system?"
- "What are the key findings about market trends?"
- "What safety procedures should I follow?"

Performance Optimization

Chunking Strategy

{
  "optimization": {
    "chunk_size": 800,
    "overlap": 100,
    "split_by_sentence": true,
    "preserve_metadata": true
  }
}

Retrieval Tuning

  • Adjust similarity thresholds

  • Optimize chunk sizes

  • Fine-tune retrieval count

  • Implement query expansion

Step 8: Production Deployment

Scalability Considerations

Vector Database Scaling

  • Monitor index size

  • Plan for growth

  • Implement data retention

  • Optimize query performance

Cost Management

cost_optimization:
  embedding_caching: true
  batch_processing: true
  compression: enabled
  retention_policy: 90_days

Security and Privacy

Data Protection

  • Encrypt stored documents

  • Implement access controls

  • Audit document access

  • GDPR compliance measures

Content Filtering

  • Sensitive information detection

  • PII removal

  • Content moderation

  • Access logging

Advanced Use Cases

Multi-Modal Documents

Image Processing

  • Extract text from images (OCR)

  • Process charts and diagrams

  • Handle mixed content

Table Processing

  • Structured data extraction

  • Table-aware chunking

  • Preserve relationships

Domain-Specific Features

Legal Documents

  • Citation tracking

  • Case law references

  • Regulation compliance

Medical Documents

  • Medical terminology handling

  • HIPAA compliance

  • Clinical decision support

Technical Documentation

  • Code snippet extraction

  • API reference handling

  • Version tracking

Integration Examples

Web Application Integration

Frontend Code

async function askQuestion(question, documentId) {
  const response = await fetch('/api/qa', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${apiToken}`
    },
    body: JSON.stringify({
      question: question,
      document_filter: documentId,
      include_citations: true
    })
  });
  
  const result = await response.json();
  return {
    answer: result.answer,
    sources: result.citations,
    confidence: result.confidence
  };
}

Slack Bot Integration

Slack Command

@slack_app.command("/ask")
def ask_documents(ack, body, client):
    ack()
    
    question = body.get('text')
    user_id = body.get('user_id')
    
    # Call BroxiAI workflow
    answer = broxi_client.run_workflow(
        workflow_id="document-qa",
        input={
            "question": question,
            "user_context": user_id
        }
    )
    
    client.chat_postMessage(
        channel=body.get('channel_id'),
        text=f"Answer: {answer['response']}\n\nSources: {answer['citations']}"
    )

Troubleshooting

Common Issues

Poor Retrieval Quality

  • Adjust chunk sizes

  • Improve document preprocessing

  • Tune similarity thresholds

  • Enhance query processing

Slow Response Times

  • Optimize vector search

  • Implement caching

  • Reduce context size

  • Use faster models

Inaccurate Answers

  • Improve prompt engineering

  • Enhance retrieval quality

  • Add answer validation

  • Implement confidence scoring

Performance Monitoring

Key Metrics

metrics:
  retrieval_quality:
    - precision@k
    - recall@k
    - mrr (mean reciprocal rank)
  
  user_satisfaction:
    - answer_relevance
    - source_citation_accuracy
    - response_time
  
  system_performance:
    - query_latency
    - indexing_speed
    - storage_efficiency

Next Steps

Enhance your document Q&A system:

  1. Multi-Language Support: Handle documents in multiple languages

  2. Advanced Analytics: Track usage patterns and optimize

  3. Custom Models: Fine-tune models for your domain

  4. Integration: Connect with business systems

Related Examples

You've built a sophisticated document Q&A system! This foundation can be extended for various use cases like customer support, research assistance, and knowledge management.

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