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

• Customer Support: Apply Q&A to support knowledge

• Content Generation: Generate content from documents

• Data Analysis: Analyze document insights

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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.