deployment-taskflow / test_api_manual.py
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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" βœ“ 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" βœ“ 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(" βœ“ 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" βœ“ Answer generated")
print(f" Uncertain: {response.uncertain}")
print(f" Answer: {response.answer[:200]}...")
except Exception as e:
print(f" βœ— Error: {type(e).__name__}: {str(e)}")
import traceback
traceback.print_exc()