rag test script

multi_llm_chatbot_backend/app/tests/test_rag_system.py

@@ -0,0 +1,155 @@
+import requests
+import json
+import time
+
+BASE_URL = "http://localhost:8000"
+
+def test_rag_system():
+    """
+    Comprehensive test of the RAG system
+    """
+    print("🚀 Testing RAG System Integration\n")
+    
+    # Test 1: Upload a document
+    print("📄 Test 1: Uploading a document...")
+    
+    # Create a sample research document
+    sample_document = """
+    Research Methodology for Machine Learning PhD
+    
+    Abstract: This document outlines the methodology for conducting research in machine learning, 
+    focusing on experimental design, data collection, and validation techniques.
+    
+    1. Introduction
+    Machine learning research requires rigorous methodology to ensure reproducible results.
+    The theoretical framework must be grounded in statistical learning theory.
+    
+    2. Methodology
+    Our research design follows a mixed-methods approach combining quantitative experiments
+    with qualitative analysis. Data collection involves sampling from multiple datasets
+    to ensure statistical validity and external validity.
+    
+    3. Practical Implementation
+    The implementation strategy focuses on actionable steps: first, establish baseline models,
+    then implement incremental improvements, and finally conduct comprehensive evaluation.
+    Each step should have clear success metrics and timelines.
+    
+    4. Theoretical Framework
+    The conceptual foundation draws from information theory, statistical learning theory,
+    and computational complexity theory. The epistemological assumptions underlying
+    our approach assume that knowledge can be extracted from data through systematic analysis.
+    
+    5. Next Steps
+    Immediate action items include: data preprocessing, model selection, hyperparameter tuning,
+    and result validation. The timeline should prioritize high-impact activities first.
+    """
+    
+    # Save as temporary file
+    with open("temp_research_doc.txt", "w") as f:
+        f.write(sample_document)
+    
+    # Upload the document
+    with open("temp_research_doc.txt", "rb") as f:
+        files = {"file": ("research_methodology.txt", f, "text/plain")}
+        upload_response = requests.post(f"{BASE_URL}/upload-document", files=files)
+    
+    print(f"Upload Status: {upload_response.status_code}")
+    print(f"Response: {json.dumps(upload_response.json(), indent=2)}\n")
+    
+    # Test 2: Get document statistics
+    print("📊 Test 2: Getting document statistics...")
+    stats_response = requests.get(f"{BASE_URL}/document-stats")
+    print(f"Stats: {json.dumps(stats_response.json(), indent=2)}\n")
+    
+    # Test 3: Test direct document search
+    print("🔍 Test 3: Testing direct document search...")
+    
+    search_queries = [
+        {"query": "What methodology should I use?", "persona": "methodist"},
+        {"query": "What is the theoretical framework?", "persona": "theorist"},
+        {"query": "What are the next steps?", "persona": "pragmatist"}
+    ]
+    
+    for search in search_queries:
+        print(f"\nSearching: '{search['query']}' for {search['persona']}")
+        search_response = requests.post(
+            f"{BASE_URL}/search-documents",
+            json=search
+        )
+        
+        if search_response.status_code == 200:
+            results = search_response.json()
+            print(f"Found {results['results_count']} results")
+            for i, result in enumerate(results['results'][:2], 1):  # Show top 2
+                print(f"  Result {i}: {result['text'][:100]}... (score: {result['relevance_score']:.3f})")
+        else:
+            print(f"Search failed: {search_response.status_code}")
+    
+    print("\n" + "="*60)
+    
+    # Test 4: Test RAG-enhanced chat
+    print("💬 Test 4: Testing RAG-enhanced chat responses...\n")
+    
+    chat_queries = [
+        "I need help with my research methodology. What approach should I take?",
+        "Can you explain the theoretical framework I should consider?", 
+        "What are the practical next steps I should focus on?"
+    ]
+    
+    for query in chat_queries:
+        print(f"Query: {query}")
+        chat_response = requests.post(
+            f"{BASE_URL}/chat-sequential",
+            json={"user_input": query, "response_length": "short"}
+        )
+        
+        if chat_response.status_code == 200:
+            result = chat_response.json()
+            print(f"Response type: {result.get('type')}")
+            
+            if 'rag_info' in result:
+                rag_info = result['rag_info']
+                print(f"RAG Info: {rag_info['personas_using_documents']}/{len(result.get('responses', []))} personas used documents")
+                print(f"Total chunks used: {rag_info['total_document_chunks_used']}")
+            
+            # Show one advisor response
+            if 'responses' in result and result['responses']:
+                advisor = result['responses'][0]
+                print(f"\n{advisor['persona_name']}: {advisor['response'][:200]}...")
+                if advisor.get('used_documents'):
+                    print(f"✅ Used {advisor.get('document_chunks_used', 0)} document chunks")
+                else:
+                    print("❌ No documents used")
+        else:
+            print(f"Chat failed: {chat_response.status_code}")
+        
+        print("\n" + "-"*40 + "\n")
+    
+    # Test 5: Session statistics
+    print("📈 Test 5: Getting comprehensive session statistics...")
+    session_stats = requests.get(f"{BASE_URL}/session-stats")
+    if session_stats.status_code == 200:
+        stats = session_stats.json()
+        print(f"Session Stats:")
+        print(f"  Messages: {stats.get('message_count', 0)}")
+        print(f"  Uploaded files: {len(stats.get('uploaded_files', []))}")
+        print(f"  RAG documents: {stats.get('rag_stats', {}).get('total_documents', 0)}")
+        print(f"  RAG chunks: {stats.get('rag_stats', {}).get('total_chunks', 0)}")
+    
+    # Cleanup
+    import os
+    try:
+        os.remove("temp_research_doc.txt")
+    except:
+        pass
+    
+    print("\n🎉 RAG System Testing Complete!")
+    print("\nKey Improvements:")
+    print("✅ Documents are now chunked and stored in vector database")
+    print("✅ Each advisor gets persona-specific document chunks") 
+    print("✅ Only relevant content is retrieved for each query")
+    print("✅ Session context is much more efficient")
+    print("✅ Supports semantic search across all uploaded documents")
+
+if __name__ == "__main__":
+    test_rag_system()