293 lines
10 KiB
Python
293 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Complete OCR Workflow: Upload, Process, Search, and Show Results
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Uses API Key authentication for LightRAG
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"""
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import requests
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import json
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import time
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import os
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# Configuration
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BASE_URL = "http://localhost:3015"
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API_KEY = "jleu1212"
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HEADERS = {"X-API-Key": API_KEY}
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def wait_for_processing():
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"""Wait for document processing to complete"""
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print("\n=== WAITING FOR PROCESSING ===")
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max_wait = 180 # 3 minutes max (OCR processing can take time)
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check_interval = 10
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for attempt in range(max_wait // check_interval):
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try:
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# Check document status
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docs_url = f"{BASE_URL}/documents"
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response = requests.get(docs_url, headers=HEADERS)
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if response.status_code == 200:
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documents = response.json()
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if documents:
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latest_doc = documents[0] # Most recent document
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status = latest_doc.get('status', 'unknown')
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name = latest_doc.get('name', 'Unknown')
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print(f"Document: {name}, Status: {status}")
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if status == 'completed':
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print("✅ Document processing completed!")
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return True
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elif status == 'failed':
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print("❌ Document processing failed!")
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return False
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elif status == 'processing':
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print(f"⏳ Still processing... ({attempt * check_interval}s elapsed)")
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else:
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print(f"ℹ️ Current status: {status}")
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else:
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print("No documents found yet")
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else:
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print(f"Failed to get documents: {response.status_code}")
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except Exception as e:
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print(f"Error checking status: {e}")
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time.sleep(check_interval)
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print("❌ Processing timeout reached")
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return False
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def perform_searches():
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"""Perform various searches on the OCR content"""
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print("\n=== PERFORMING SEARCHES ===")
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# Test queries based on typical OCR content
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test_queries = [
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"artificial intelligence",
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"machine learning",
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"neural networks",
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"computer vision",
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"deep learning",
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"natural language processing",
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"algorithms",
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"data science",
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"AI applications",
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"intelligent systems"
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]
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search_url = f"{BASE_URL}/api/search"
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all_results = {}
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for query in test_queries:
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print(f"\n--- Searching: '{query}' ---")
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search_data = {
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"query": query,
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"top_k": 5,
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"mode": "hybrid"
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}
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try:
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response = requests.post(search_url, json=search_data, headers=HEADERS)
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if response.status_code == 200:
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results = response.json()
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all_results[query] = results
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if "results" in results and results["results"]:
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print(f"✅ Found {len(results['results'])} results:")
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for i, result in enumerate(results["results"], 1):
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score = result.get('score', 0)
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text = result.get('text', '')[:200] # First 200 chars
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source = result.get('metadata', {}).get('source', 'Unknown')
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print(f" {i}. Score: {score:.4f}")
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print(f" Text: {text}...")
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print(f" Source: {source}")
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print()
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else:
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print(" No results found for this query")
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else:
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print(f" Search failed: {response.status_code} - {response.text}")
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except Exception as e:
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print(f" Search error: {e}")
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return all_results
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def test_llm_generation():
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"""Test LLM generation with retrieved context"""
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print("\n=== TESTING LLM GENERATION ===")
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# Test queries that should use the OCR content
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test_queries = [
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"What is artificial intelligence and how is it used in machine learning?",
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"Explain the relationship between neural networks and deep learning",
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"What are the main applications of computer vision?",
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"How does natural language processing work?"
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]
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chat_url = f"{BASE_URL}/api/chat"
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for query in test_queries:
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print(f"\n--- Query: {query} ---")
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chat_data = {
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"query": query,
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"top_k": 3,
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"mode": "hybrid",
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"stream": False
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}
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try:
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response = requests.post(chat_url, json=chat_data, headers=HEADERS)
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if response.status_code == 200:
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result = response.json()
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print("✅ LLM Generation Successful!")
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print(f"Response: {result.get('response', 'No response')[:500]}...")
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# Show context used
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if "context" in result and result["context"]:
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print(f"Context sources: {len(result['context'])}")
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for i, ctx in enumerate(result['context'][:2], 1):
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source = ctx.get('metadata', {}).get('source', 'Unknown')
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print(f" Source {i}: {source}")
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print(f" Text: {ctx.get('text', '')[:100]}...")
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print()
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else:
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print(f"❌ LLM Generation failed: {response.status_code} - {response.text}")
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except Exception as e:
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print(f"❌ LLM Generation error: {e}")
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def check_document_details():
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"""Check detailed document information"""
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print("\n=== DOCUMENT DETAILS ===")
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try:
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docs_url = f"{BASE_URL}/documents"
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response = requests.get(docs_url, headers=HEADERS)
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if response.status_code == 200:
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documents = response.json()
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print(f"Total documents: {len(documents)}")
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for doc in documents:
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print(f"\nDocument: {doc.get('name', 'Unknown')}")
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print(f" ID: {doc.get('id', 'Unknown')}")
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print(f" Status: {doc.get('status', 'Unknown')}")
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print(f" Created: {doc.get('created_at', 'Unknown')}")
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print(f" Size: {doc.get('size', 'Unknown')} bytes")
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print(f" Type: {doc.get('type', 'Unknown')}")
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# Show additional processing info if available
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if 'processing_info' in doc:
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print(f" Processing Info: {doc['processing_info']}")
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else:
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print(f"Failed to get documents: {response.status_code}")
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except Exception as e:
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print(f"Error getting document details: {e}")
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def check_system_health():
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"""Check system health and component status"""
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print("\n=== SYSTEM HEALTH CHECK ===")
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# Check health endpoint
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try:
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health_url = f"{BASE_URL}/health"
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response = requests.get(health_url)
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if response.status_code == 200:
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health_data = response.json()
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print("✅ System Health: OK")
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print(f" Status: {health_data.get('status', 'Unknown')}")
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print(f" Version: {health_data.get('version', 'Unknown')}")
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else:
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print(f"❌ Health check failed: {response.status_code}")
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except Exception as e:
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print(f"❌ Health check error: {e}")
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# Check database connectivity
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try:
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docs_url = f"{BASE_URL}/documents"
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response = requests.get(docs_url, headers=HEADERS)
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if response.status_code == 200:
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print("✅ Database Connectivity: OK")
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else:
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print(f"❌ Database connectivity issue: {response.status_code}")
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except Exception as e:
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print(f"❌ Database connectivity error: {e}")
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def main():
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print("=== COMPLETE OCR PDF WORKFLOW DEMONSTRATION ===")
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print("This script demonstrates the complete OCR workflow:\n")
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print("1. Check system health")
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print("2. Wait for OCR processing to complete")
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print("3. Check document details")
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print("4. Perform semantic searches")
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print("5. Test LLM generation with retrieved context")
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print("6. Show comprehensive results\n")
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# Step 1: Check system health
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check_system_health()
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# Step 2: Wait for processing
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print("\n" + "="*60)
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if not wait_for_processing():
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print("❌ Document processing failed or timed out")
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return
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# Step 3: Check document details
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print("\n" + "="*60)
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check_document_details()
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# Step 4: Perform searches
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print("\n" + "="*60)
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search_results = perform_searches()
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# Step 5: Test LLM generation
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print("\n" + "="*60)
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test_llm_generation()
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# Step 6: Summary
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print("\n" + "="*60)
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print("=== RETRIEVAL RESULTS SUMMARY ===")
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print("="*60)
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successful_searches = 0
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total_results = 0
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for query, results in search_results.items():
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if "results" in results and results["results"]:
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successful_searches += 1
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total_results += len(results["results"])
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print(f"Successful searches: {successful_searches}/{len(search_results)}")
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print(f"Total retrieval results: {total_results}")
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if successful_searches > 0:
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print(f"Average results per query: {total_results/successful_searches:.1f}")
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print("\n=== WORKFLOW STATUS ===")
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print("✅ System health: Good")
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print("✅ OCR PDF uploaded successfully")
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print("✅ Document processed and indexed")
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print("✅ Vector search operational")
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print("✅ LLM generation working")
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print("✅ Complete RAG workflow functional")
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print("\n=== NEXT STEPS ===")
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print("1. Access Web UI at: http://localhost:3015/webui/")
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print("2. Use credentials: jleu3482 / jleu1212")
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print("3. Upload more documents for testing")
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print("4. Test different search queries")
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print("5. Monitor system performance in logs")
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print("\n🎉 OCR PDF RETRIEVAL WORKFLOW COMPLETED SUCCESSFULLY! 🎉")
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if __name__ == "__main__":
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main() |