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| """Manual test script for debugging API issues.""" | |
| import os | |
| from dotenv import load_dotenv | |
| from src.rag import RAGSystem | |
| from src.llm_client import LLMClient | |
| from src.escalation import EscalationEngine | |
| # Load environment | |
| load_dotenv() | |
| print("Testing API components...") | |
| print(f"API Key present: {bool(os.getenv('GOOGLE_API_KEY'))}") | |
| print(f"API Key value: {os.getenv('GOOGLE_API_KEY')[:20]}..." if os.getenv('GOOGLE_API_KEY') else "None") | |
| # Initialize RAG | |
| print("\n1. Initializing RAG system...") | |
| rag = RAGSystem("data/articles") | |
| print(f" ✓ Loaded {rag.get_stats()['total_chunks']} chunks") | |
| # Test retrieval | |
| print("\n2. Testing retrieval...") | |
| question = "What subscription plans are available?" | |
| chunks = rag.retrieve(question, top_k=3) | |
| print(f" ✓ Retrieved {len(chunks)} chunks") | |
| for i, chunk in enumerate(chunks): | |
| print(f" Chunk {i+1}: score={chunk.score:.3f}, source={chunk.source}") | |
| print(f" Content: {chunk.content[:100]}...") | |
| # Test LLM | |
| print("\n3. Testing LLM client...") | |
| try: | |
| llm = LLMClient(model_name="gemini-2.5-flash") | |
| print(" ✓ LLM client initialized") | |
| # Try to generate answer | |
| print("\n4. Generating answer...") | |
| chunk_contents = [c.content for c in chunks] | |
| response = llm.generate_answer(question, chunk_contents) | |
| print(f" ✓ Answer generated") | |
| print(f" Uncertain: {response.uncertain}") | |
| print(f" Answer: {response.answer[:200]}...") | |
| except Exception as e: | |
| print(f" ✗ Error: {type(e).__name__}: {str(e)}") | |
| import traceback | |
| traceback.print_exc() | |