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| import sys | |
| import os | |
| import asyncio | |
| # Add current directory to path | |
| sys.path.append(os.getcwd()) | |
| from app.core.config import settings | |
| from app.services.enrichment import AIEnrichmentService | |
| from app.services.generation import GenerationService | |
| async def verify_models(): | |
| print("--- Verifying Local Models ---") | |
| print(f"USE_LOCAL_MODELS: {settings.USE_LOCAL_MODELS}") | |
| print(f"Effective LLM: {settings.get_effective_llm()}") | |
| print(f"Effective Embedding: {settings.get_effective_embedding()}") | |
| enricher = AIEnrichmentService() | |
| generator = GenerationService() | |
| # 1. Test Embedding | |
| print("\n1. Testing Local Embedding...") | |
| text = "Hello world" | |
| embedding = await enricher.generate_embedding(text) | |
| print(f"Embedding generated! Length: {len(embedding)}") | |
| assert len(embedding) == 384, f"Expected 384, got {len(embedding)}" | |
| # 2. Test Generation | |
| print("\n2. Testing Local Generation (Lightweight)...") | |
| query = "Who are you?" | |
| context = [{"payload": {"text": "I am a local AI assistant."}}] | |
| response = await generator.generate_answer(query, context) | |
| print(f"Answer: {response['answer']}") | |
| print(f"Citations: {response['citations']}") | |
| if __name__ == "__main__": | |
| asyncio.run(verify_models()) | |