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function full_reading_example

Maturity: 44

Demonstrates the full reading mode of a RAG (Retrieval-Augmented Generation) system by processing all documents to answer a comprehensive query about key findings.

File:
/tf/active/vicechatdev/docchat/example_usage.py
Lines:
103 - 132
Complexity:
simple

Purpose

This function serves as a demonstration/example of how to use the DocChatRAG system in 'full_reading' mode, which processes all available documents rather than using selective retrieval. It showcases progress tracking, result formatting, and metadata extraction. This mode is useful when comprehensive analysis across all documents is needed, though it takes longer (1-5 minutes) than standard retrieval modes.

Source Code

def full_reading_example():
    """Example: Use Full Reading mode"""
    print("\n=== Full Reading Mode Example ===\n")
    
    rag = DocChatRAG()
    
    query = "What are all the key findings across all documents?"
    
    print(f"Query: {query}\n")
    print("Processing all documents (this may take 1-5 minutes)...\n")
    
    # Progress callback
    def show_progress(message):
        print(f"  → {message}")
    
    result = rag.chat(
        query=query,
        mode="full_reading",
        progress_callback=show_progress
    )
    
    print("\nResponse:")
    print("-" * 80)
    print(result['response'])
    print("-" * 80)
    
    print(f"\nMetadata:")
    print(f"  Mode: {result['mode']}")
    print(f"  Total documents: {result.get('num_documents', 0)}")
    print(f"  Relevant documents: {result.get('num_relevant_documents', 0)}")

Return Value

This function does not return any value (implicitly returns None). It prints output directly to the console, including the query, processing progress updates, the generated response, and metadata about the operation (mode used, total documents processed, and number of relevant documents found).

Dependencies

  • pathlib
  • document_indexer
  • rag_engine
  • config

Required Imports

from pathlib import Path
from document_indexer import DocumentIndexer
from rag_engine import DocChatRAG
import config

Usage Example

# Ensure documents are indexed and config is set up
# Then simply call the function:
full_reading_example()

# Expected output:
# === Full Reading Mode Example ===
# Query: What are all the key findings across all documents?
# Processing all documents (this may take 1-5 minutes)...
#   → [progress messages]
# Response:
# --------------------------------------------------------------------------------
# [Generated response with key findings]
# --------------------------------------------------------------------------------
# Metadata:
#   Mode: full_reading
#   Total documents: 10
#   Relevant documents: 8

Best Practices

  • This is a demonstration function meant to be run as an example, not integrated into production code
  • The full_reading mode processes all documents and can take 1-5 minutes - use sparingly and only when comprehensive analysis is needed
  • Consider implementing timeout handling for production use cases
  • The progress_callback pattern shown here is useful for long-running operations to provide user feedback
  • Ensure documents are properly indexed before calling this function
  • For production use, extract the core logic and add error handling around the rag.chat() call
  • Consider adding logging instead of print statements for production environments

Tags

example demonstration RAG retrieval-augmented-generation full-reading document-processing progress-tracking chatbot NLP document-analysis comprehensive-search

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